EREADING WP 1 VISUAL POWER D1.2.3.3 MARKET DATA VISUALIZATION – CONCEPTS, TECHNIQUES AND TOOLS
D1.2.3.3 Market Data Visualization – Concepts, Techniques and Tools
Authors: Mari Laine-Hernandez (Aalto SCI) & Nanna Särkkä (Aalto ARTS)
Confidentiality: Public
Date and status: 1.11.2013 - Status: Final
This work was supported by TEKES as part of the next Media programme of TIVIT (Finnish Strategic Centre for Science, Technology and Innovation in the field of ICT) Next Media – a Tivit Programme Phase 4 (1.1.–31.12.2013)
Participants Name Organisation
Responsible partner Mari Laine-Hernandez Aalto SCI/Media
Responsible partner Nanna Särkkä Aalto ARTS/Media
next Media www.nextmedia.fi www.tivit.fi
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Executive Summary
This report looks at the visualizations of abstract numerical data: what types of visualizations exist, how they can be conceptualized, and what kind of techniques and tools there are available in practice to produce them. More specifically the focus is on the visualizations of market data in journalistic context and on digital platforms. The main target group of the visualizations we are looking at are mainly small- scale investors (also entrepreneurs and decision makers) whose level of expertise varies. The report discusses the ways in which complicated numerical data can be presented in an efficient, illustrative manner, so that relevant up-to-date information is quickly available for those who want to keep up with the finances. We suggest a fourfold table model for the classification of interactive visualizations, for which the current one-dimensional classifications are insufficient. The model combines the role of the reader (as a passive receiver or as an active explorer) and the focus of the visualization (whether it is on data or on user). We have observed that there are surprisingly few innovative visualizations of market data. Instead, the majority of the realizations are quite simple. We discuss the reasons for this: the rational-scientific orientation of stock market information and its use, and the fact that production of infographics challenges the established processes and work division of journalism. We also provide solutions: in chapter 5 we list nine existing tools for producing interactive data visualizations. Our aspiration is that media companies would have the wisdom to invest on new narrative forms of communication, such as interactive infographics. Nowadays information is available for free for anyone. Publications cannot justify their existence by just presenting some of this data; instead they need to process it further, for example into interactive information graphics where users can explore it by them selves. Due to the dynamic and interactive quality of the visualizations at issue and the fact that a paper format is not quite the ideal format for discussing them, this report includes many links to online sources. This report is one of the outcomes of a project in which the aim is to develop new ways for presenting stock market data in collaboration with the Finnish financial media company Kauppalehti.
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Table of Contents
Executive Summary ...... 2 Table of Tables ...... 5 Table of Figures ...... 6 1. Introduction ...... 8 1.1 Definitions ...... 8 1.2 Structure of the Report ...... 10 2. Characterizing Visualizations...... 11 2.1 Why Visualize? ...... 11 2.2 The Objectives of Visualizations ...... 12 2.2.1 To Present or to be Explored? ...... 12 2.2.2 Different Objectives in Different Application Areas ...... 13 2.3 Requirements for Visualizations ...... 14 2.3.1 Utility, Soundness and Attractiveness ...... 14 2.3.2 Scientific Orientation vs. Journalistic Orientation ...... 15 2.4 Quality Criteria for Visualizations ...... 16 2.5 Classifying Visualizations ...... 17 2.5.1 Classification Frameworks ...... 18 2.5.2 Choice of Encoding Mechanism ...... 19 2.5.3 Dynamic Data ...... 22 2.5.4 Interaction ...... 24 2.6 Fourfold Table of Market Data Visualizations...... 26 3. Visualization Techniques for Market Data ...... 28 3.1 Basic Techniques ...... 28 3.1.1 Pie Charts ...... 28 3.1.2 Bar Charts ...... 29 3.2 Visualizing Time-Oriented Data ...... 30 3.2.1 Line Charts...... 30 3.2.2 Open-High-Low-Close (OHLC) Charts ...... 31 3.2.3 Candlestick Charts ...... 32 3.2.4 Index Charts ...... 32 3.3 Space-Economical Visualizations ...... 34 3.3.1 Small Multiples ...... 34 3.3.2 Sparklines ...... 35 3.3.3 Pixel Bar Charts ...... 36 3.3.4 Horizon Graphs ...... 37 3.4 Overview Visualizations ...... 38 3.4.1 Scatter Plots...... 38 3.4.2 Heatmaps...... 39
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3.5 Visualizing Hierarchical Data ...... 39 3.5.1 Treemaps ...... 40 3.5.2 Sunbursts ...... 41 3.6 Visualization Aids ...... 41 4. Methods for Evaluating Information Visualization ...... 43 4.1 Heuristic Evaluation ...... 43 4.2 User Tests...... 43 4.3 Eye Tracking ...... 44 5. Tools for Visualizing Market Data ...... 46 5.1 AmCharts ...... 46 5.2 AnyChart ...... 47 5.3 D3 ...... 49 5.4 Envision.js ...... 50 5.5 Flot ...... 51 5.6 FusionCharts...... 52 5.7 Highcharts ...... 54 5.8 jqPlot ...... 55 5.9 Tableau ...... 56 6. Conclusions ...... 59 6.1 Rational Needs of the Users ...... 59 6.2 Challenge to News Rooms...... 59 6.3 Shift from Author-driven to Reader-driven?...... 60 References ...... 62
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Table of Tables
Table 1 The order of the objectives of visualizations in different application areas...... 13 Table 2 Taxonomy of dynamic data visualization (Cottam et al., 2012)...... 23 Table 3 Author-driven vs. reader-driven visualizations (Segel & Heer 2010) ...... 26 Table 4 Taxonomy of interactive dynamics (Heer & Shneiderman, 2012) ...... 26 Table 5 Actions and tasks for evaluating stock data visualization techniques (Merino et al. (2006) ...... 44 Table 6 Eye tracking measures (Calitz et al., 2009) ...... 44
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Table of Figures
Figure 1 Infographic Formats Quadrant (Lankow et al., 2012) ...... 18 Figure 2 Bertin’s guidance regarding the suitability of different encoding mechanisms for common information visualization tasks (Spence 2007, 52)...... 20 Figure 3 Choosing the right chart (Abela 2010)...... 21 Figure 4 Taxonomy of dynamic data visualization (Cottam et al., 2012)...... 23 Figure 5 Market Watch’s Map of the Market (pictured here in part), http://www.marketwatch.com/tools/stockresearch/marketmap, 23 September 2013...... 24 Figure 6 Fourfold table of market data ...... 27 Figure 7 Pie chart (http://www.expansion.com/mercados/micartera.html?cid=BA18001&s_kw=Mi- cartera-etools) ...... 28 Figure 8 The same data in a bar chart for comparison ...... 29 Figure 9 The “Sector barometer”, a horizontal bar chart displaying the rate of change per industrial sector, Dagens Næringsliv (http://www.dn.no/finans/portal/marketOverview- nordic) ...... 29 Figure 10 Stacked bar chart visualizing the recommendations of 41 investment analysts regarding Nokia Oyj stocks, Financial Times (http://markets.ft.com/research/Markets/Tearsheets/Forecasts?s=NOK1V:HEX) ...... 30 Figure 11 Line chart visualization of the Apple Inc stock, Reuters.com (http://www.reuters.com/finance/stocks/chart?symbol=AAPL.O) ...... 31 Figure 12 OHLC chart of the Apple Inc stock, Reuters.com (http://www.reuters.com/finance/stocks/chart?symbol=AAPL.O) ...... 31 Figure 13 Candlestick Red/Green chart of the Apple Inc stock, Reuters.com (http://www.reuters.com/finance/stocks/chart?symbol=AAPL.O) ...... 32 Figure 14 Index chart comparing the development of Microsoft and Apple’s stock prices during the last year (Source: Bloomberg, http://www.bloomberg.com/quote/MSFT:US/chart)...... 33 Figure 15 Index Chart of Selected Technology Stocks, 2000-2010 (Heer et al. (2010, data source: Yahoo! Finance), see the interactive version at: http://hci.stanford.edu/jheer/files/zoo/ex/time/index-chart.html ...... 34 Figure 16 Small multiples in MarketWatch (http://www.marketwatch.com/myportfolio) ...... 35 Figure 17 Stock quotes visualized with bar chart sparklines in Bloomberg Businessweek (http://www.businessweek.com/markets-and-finance) ...... 35 Figure 18 Key currencies visualized with line graph sparklines in Market Watch (http://www.marketwatch.com/investing/currencies) ...... 36 Figure 19 Visualizations of the Ameris Bancorp share, from the top: line chart of absolute value; bar chart of relative percentage change; colour-coded pixel bar chart reflecting relative
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percentage change and price volatility; pixel bar chart averaged over each three pixels (Ziegler et al., 2010)...... 37 Figure 20 (a) Filled line chart, (b) Mirrored chart, (c) 2-band horizon graph (Heer, Kong, & Agrawala, 2009) ...... 38 Figure 22 A scatter plot displaying stock price and its percentage change. The colour of each dot indicates time: the darker the earlier. (Lei & Zhang, 2010) ...... 39 Figure 23 A heatmap visualization of the Dow Jones Industrial Average stock market index by De Tijd (http://www.tijd.be/geld_en_beleggen) ...... 39 Figure 24 Smart Money’s Map of the Market (http://www.smartmoney.com/map-of-the- market/); a treemap visualization of stock market activity organized by industry sectors...... 40 Figure 25 Sunburst visualization in the Bloomberg iPad app...... 41 Figure 26 JavaScript Stock Chart for stock price comparisons (http://www.amcharts.com/stock-chart/) ...... 47 Figure 27 Stock Events Chart (http://www.amcharts.com/stock-chart/stock-events/) ...... 47 Figure 28 Stock Chart (http://anychart.com/products/anychart/gallery/samples/ACME-Corp- Stock-Prices.html#html-view) ...... 48 Figure 29 Horizontal Linear Gauge (http://www.anychart.com/products/anychart/gallery/samples/Horizontal-Linear-Gauge- Multiple-Color-Ranges.html#html-view) ...... 48 Figure 30 Zoomable Treemap (http://mbostock.github.io/d3/talk/20111018/treemap.html) ... 49 Figure 31 Infographics in The New York Times implemented using D3 (http://www.nytimes.com/interactive/2013/04/08/business/global/asia-map.html) ...... 50 Figure 32 HTML5 Finance Template (http://www.humblesoftware.com/envision/demos/finance) ...... 51 Figure 33 An interactive chart with a vertical crosshair (http://www.flotcharts.org/flot/examples/tracking/index.html) ...... 52 Figure 34 Interactive Candlestick Chart (http://www.fusioncharts.com/demos/gallery/#candlestick-chart) ...... 53 Figure 35 Heat Map Chart for Conversion Rates (the pointers in the gradient scale can be dragged to see data sets lying in a selected range only) (http://www.fusioncharts.com/demos/gallery/#heat-map-chart) ...... 54 Figure 36 Apple Inc Stock Chart implemented using Highstock (http://www.highcharts.com/stock/demo/line-markers) ...... 55 Figure 37 OHLC Chart (http://www.jqplot.com/tests/candlestick-charts.php) ...... 56 Figure 38 “World GDP Through Time” (http://www.tableausoftware.com/learn/gallery/world-gdp-and-business) ...... 57 Figure 39 Candlestick Chart of the Coca Cola stock (http://www.tableausoftware.com/learn/gallery/coke-stock-price) ...... 57 Figure 40 Fourfold table about the visualization of market data...... Error! Bookmark not defined.
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1. Introduction
Information visualization is currently a hot topic. Considering the abundance of information we are surrounded by, finding efficient and appealing ways to communicate ones message is getting vital. Understanding about the ways humans perceive and process visualizations has increased, and due to digitalization, massive amounts of data are available and also the necessary tools and software are at hand for anyone to use1 (Cairo 2013a, 14). This report looks at the visualizations of abstract numerical data: what different types of visualizations there are, how these visualizations can be conceptualized, and what kind of techniques and tools there are available in practice to produce them. More specifically the focus is on the visualizations of market data in journalistic context and published on digital platforms. Because of the journalistic context, the main target group of the visualizations we are looking at are not professionals (they have their own, more sophisticated tools for data acquisition), but mainly small-scale investors (also entrepreneurs and decision makers). The report describes and discusses the ways in which complicated numerical data can be presented in an efficient, illustrative manner, so that relevant up-to-date information is quickly available for those who want to keep up with the finances. One can make a distinction between visualizations that are supposed to support journalistic text (text has the primary role), and independent visualizations (e.g. Segel & Heer 2010). Our focus is on visualizations that can have an independent role on digital platforms. The goal of this report is to present an overview of information visualization and the most important concepts related to the field, and to provide knowledge that can be used in the development and evaluation of visualizations. This report is one of the outcomes of a project in which the aim is to develop new ways for presenting stock market data in collaboration with the Finnish financial media company Kauppalehti. The examples in this report originate from the benchmarking of market data visualizations carried out in the spring of 2013. Altogether 44 web sites and 29 tablet or mobile applications (mainly financial media’s) from 12 countries were surveyed. A separate report describing the entire project, including a usability evaluation of the current website, a user survey and the results of the benchmarking, will be published later in 2013.2
1.1 Definitions
Data visualization in journalistic context is above all a multidisciplinary issue. It involves at least graphic design, information and computer sciences, human- computer interaction, statistics, data mining, illustration, cartography and basic
1 E.g. http://openrefine.org 2 Deliverable 1.2.3.4 Development of market data presentations
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journalistic principles of addressing the reader in the best possible way (see e.g. Cairo 2013a). Visualizations are produced and discussed in scientific, pragmatic and artistic contexts (e.g. Lankow, Ritchie & Crooks 2012; Kosara 2007). As Steve Duanes, the graphic director of the New York Times, has said, “information graphics should be the right mix of art, journalism and science” (Giardina et Medina 2013, 5). The multidisciplinary character of the field is one reason for the large variety of concepts and approaches. Terms like information visualization, data visualization, information graphics, infographics and information illustration are used and not always in a coherent manner (see e.g. Järvi 2006, 59; Lankow, Ritchie & Crooks 2012, 19). Some writers use different terms for presentations where the readers’ role is more active or passive. For example, some make the following distinction: infographics present information by means of statistical charts, maps, and diagrams and tell stories designed by communicators, while information visualization offers visual tools that an audience can use to explore and analyse data sets themselves. In the latter the data visualization is above all an interface to the data. (Cairo 2013a, xv.) The traditional dictionary definition of visualization is “formation of mental visual images”3. According to Ware (2004), however, visualization has recently taken on the meaning of “something more like a graphical representation of data or concepts”. Spence (2007) describes information visualization as a combination of two activities: the transformation of data into pictures, which are then interpreted by a human being. Vande Moere and Purchase (2011) describe the traditional view of information visualization as “methods for supporting humans to understand and analyse large, complex data sets”. A simple definition by Lankow et al. (2012, 20) sees that infographics or information graphics are visualizations that use visual cues to communicate information; they are design-oriented, and often artistic and manually created. Alberto Cairo (2013b) on the other hand defines an infographic (or a visualization) as a visual display of evidence, a tool for analysis, understanding, or communication. Wikipedia defines data visualization as “the study of the visual representation of data”4 (meaning “information that has been abstracted in some schematic form” (Friendly, 2009)) and information visualization as “the study of (interactive) visual representations of abstract data to reinforce human cognition”5. Considering these definitions, it is not surprising that the two terms are often used interchangeably. In this report, by visualization we mean information visualization. Any visual artefact such as a photograph is a visualization, but we focus on the visualization of data (quantifiable information), and more specifically market data. We also use the concepts of information graphics and infographics, in which case the
3 http://www.merriam-webster.com/dictionary/visualization, accessed June 5, 2013 4 http://en.wikipedia.org/wiki/Data_visualization, accessed June 5, 2013 5 https://en.wikipedia.org/wiki/Information_visualization, accessed June 5, 2013
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emphasis is on non-interactive and usually print visualizations. By information designer we mean the person who designs the visualization – whether it is a graphic designer, journalist or an engineer. We mostly discuss visualizations that are generated automatically using software. This is because in the market data context a large part of the data is constantly updating, real-time information is wanted, and on digital platforms there is no time for human intervention.
1.2 Structure of the report
First, we discuss the characteristics of information visualizations, their objectives, requirements and ways to classify them (chapter 2). We then proceed to present different information visualization techniques accompanied with examples from the market data domain (chapter 3). We also review methods for evaluating information visualizations (chapter 4) and provide practical examples of tools and techniques for creating market data visualizations (chapter 5). In chapter 6 we conclude.
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2. Characterizing Visualizations
In this chapter, based on previous research and literature on infographics, we describe the characteristics of information visualizations, their objectives, requirements and existing ways to classify them. In the end of the chapter we present our fourfold table model for the classification of market data visualizations. Visual ways of communicating information have become more and more frequent in the 21st century media ecosystem, and the evolution of interactive forms of infographics has further increased the interest towards visualizing (e.g. Giardina & Medina 2013). Visualizations have received quite notable attention in research as well. Information graphics have been widely studied, and in accordance with the multidisciplinary nature of the field, the literature related to infographics approaches the topic from many different perspectives: e.g. graphic design, human-computer interaction, statistics, visual analytics, journalistic work and visual analytics. However, research about the new interactive forms of information graphic, as visual, multimedia storytelling is only at its very beginning. (Weber & Rall 2012, 1.)
2.1 Why visualize?
One of the common reasons for visualizing data is that visualization makes information more comprehensible – as Stephen Few (2013) writes, visual perception is more efficient and faster than thinking – although some scholars disagree with this argument, saying that a thorough understanding always requires time (i.e. Järvi 2006, 36). Information graphics are said to be able to make the data engaging, point out causal relationships, and tell the story behind it (Weber & Rall 2012, 1). William Playfair (1759–1823), one of the first information designers, justified the use of visualizations in the following way: Graphics can be used to simplify complicated issues. Visualizations facilitate the adoption and understanding of information. Information presented visually is better remembered. (Kuusela 2000, 27.) From the point of view of journalistic publications, one of the benefits of information visualizations is that people seem to spend more time reading visualized stories than those without any visualization (Leivonniemi 2012, 5; Holmberg & Holmqvist 2005). The new, interactive visualizations can be seen as a marketing tool: they are easily shared and linked to in social media. Interactive infographics can also be seen as an added value that online mass media can offer, unlike their conventional print media counterparts (Giardina & Medina 2013). A slightly more controversial argument supporting the use of infographics is one that sees graphics as a more neutral and objective way of transferring information (in comparison to, for example, photographs) since they lack emotions (i.e. Munk 1999; Salo 2000; Järvi 2006, 47). These kinds of views support the use of
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infographics in financial journalism, where the information offered is mainly quite raw data, based on which small-scale investors make their own conclusions regarding their investments. But it is a misleading idea that anything based on numbers is objective, because all numbers are always the result of a choice – what numbers and which phenomenon has been chosen to be presented and in which way. Numbers can be biased as well. It is, however, true that (interactive) information visualizations have the possibility to contribute to a more open journalism. They can give readers the opportunity to explore the data and judge for themselves whether they agree with the conclusions that the journalist has made (e.g. Cairo 2013a, 13).
2.2 The Objectives of Visualizations
Visualizations are generally seen as tools that help the reader see the data more clearly – as Cairo (2013a, 10) puts it, the main goal of a visualization is “to be a tool for your eyes and brain to perceive what lies behind their natural reach”. Visualizations aim at aiding “our understanding of data by leveraging the human visual system’s highly tuned ability to see patterns, spot trends, and identify outliers” (Heer, Bostock & Ogievetsky, 2010). When visualization is well implemented, the viewer does not have to carry out complicated cognitive reasoning but instead simply perceive the visualization to get an understanding of the underlying data. Good visualizations facilitate remembering aspects of the data, and provide help for decision-making. Data visualizations should also be able to “tell stories with data” (Segel & Heer, 2010). In other words, visualizations take (possibly abstract, complex and voluminous) input data and visually present it (or certain desired aspects of it) to the viewer in a way that makes it easy to perceive, process, understand and remember, but also interesting.
2.2.1 To present or to be explored?
Two kinds of intentions can be distinguished regarding the role of visualization in relation to the reader (as already mentioned in 1.1): whether they intend to communicate and present an already-defined message (where the end-user’s role is more passive, that of a receiver), or they offer the data for the end-user for exploring, in which case the visualization acts as a user interface into the data (i.e. Cairo 2013a). Cairo (2013a, xvi & 73) himself sees the two options on a continuum. Every infographic and visualization has a presentation and an exploration component: they present, but they also facilitate the analysis of what they show, to different degrees – they communicate and analyse.
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2.2.2 Different objectives in different application areas
The objectives of visualizations naturally vary according to, among others, the type and quality of data they are based on and the context in which they are published. Also different audiences have different requirements. As Munk (1999) has pointed out, people reading financial news tend to spend a lot of time with graphics and for them the information contained in the graphics is more important than the aesthetics. In the case of the Finnish financial newspaper the readers are very information oriented: they read the print publication and the website mainly in the search of pieces of data that can be useful for them in their investments or in their work. The complexity of a visualization should be adapted according to the readers (Cairo 2013a, 59). The more specialized the audience is, all the more uniform is the level of their knowledge. This however cuts both ways – if the content is adjusted to a specific level of understanding, it is no longer inviting for a broader audience. Lankow, Ritchie and Crooks (2012) name the objectives of visualizations as appeal, comprehension, and retention, and according to them the emphasis of different objectives depends on the application. For scientific visualization, for instance, comprehension is the most important objective, followed by retention and then appeal with the least priority. In marketing and editorial infographics (where publications actively compete for the readers’ attention), appeal has on the contrary highest priority, followed by comprehension in editorial visualizations, and retention in marketing (where the aim is to produce lasting effects). We postulate that for market data visualizations in non-professional use comprehension is the most important objective, appeal is the second-most important and retention has the lowest priority of the three objectives. Users’ main objective is to find essential, up-to-date information as quickly as possible. Appeal is important since there are many competing publications offering the same data. Retention is not so central in the case of the constantly updating basic market data.
Table 1 The order of the objectives of visualizations in different application areas.
Application 1st priority 2nd priority 3rd priority Scientific Comprehension Retention Appeal Editorial Appeal Comprehension Retention Marketing Appeal Retention Comprehension Market data Comprehension Appeal Retention
According to Ward and Theroux (1997) the goals of visualization are identification (of interesting structures, features and characteristics of data), classification (of the nature or type of the structure), quantification (of the size or extent of the structure), understanding (correlations between data dimensions) and comparison (of datasets in terms of structure, subsets, range of values or other
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features). For market data it is especially important to provide the user with an understanding of the data, including identification of the structures and overview of the quantification, and means to compare values (e.g. the development of a stock over time or the relationship between two or more stocks).
2.3 Requirements for visualizations
Data visualization is successful only if it encodes information in a way that our eyes can discern and our brains can understand. The aim is to translate abstract information into visual representations that can be easily, efficiently, accurately, and meaningfully decoded. (Few 2013.)
2.3.1 Utility, soundness, and attractiveness
Vande Moere and Purchase (2011) apply the three Vitruvian design requirements (published by the Roman architect Vitruvius in his book De Architectura in 25BC) to information visualizations: utility, soundness, and attractiveness. Information visualization developers must find a balance between these three requirements. Utility refers to functionality, usability, usefulness and other quantitative performance measures, defined in terms of effectiveness and efficiency. It can be described through usability evaluations (for more information about two methods commonly employed in usability evaluations, heuristic evaluations and user tests, see sections 4.1 and 4.2). The meaning of soundness for Vande Moere and Purchase (2011) is reliability and robustness (i.e. the quality of the visualization presentation algorithm), but also how well the algorithm can be used outside its initial presentation, in a wider context. On the other hand, Lankow et al. (2012) – in the context of infographics – define soundness in terms of meaning and integrity. A good infographic communicates a message that is worth telling, i.e. it is somehow of value to its viewers. The information presented is complete, trustworthy and interesting. Attractiveness mainly refers to the appeal or beauty of the visualization, but includes also originality, innovation and novelty, and other subjective factors comprising the user experience. Attractiveness has traditionally not been a central consideration in the field of scientific information visualization, and there is a clear distinction between information designers with a rational, scientific approach to information visualization and those who favour aesthetics and emotions more (Cairo 2013a, 61). Edwad Tufte (2001, 2006), the pioneer of data visualization, is a strong advocate of the first school: he prioritizes data and its effective presentation and disapproves of all unnecessary elements. He writes about chart junk, supplementary decorative elements that should be avoided, and data-ink-ratio: ideally data is presented with as little ink as possible (Tufte 2001). The opposite view is that illustrative elements can be beneficial to reading the data, making conclusions and recollection (Cairo, 2013a, 65). Nigel Holmes
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(1991, 47) criticizes Tufte for forgetting that people are not necessarily always interested in the data, concentrated on reading it, nor intelligent. He sees an illustrative approach useful in making visualization more user-friendly and understandable. Some studies have shown that attractiveness may have a positive influence on the reception of visualizations (i.e. Cairo 2013a, 66). People tend to spend more time with attractive visualizations and are more forgiving of software errors if the user interface is appealing (Cawthon & Vande Moere, 2007). According to Norman (2002), attractiveness may have a positive influence on task performance with everyday objects, and there is no reason to believe that the effect would not carry on to the area of information visualization. For instance, Bateman, Mandryk and Gutwin (2010) showed that visual embellishments that are not essential to understanding data (i.e. chart junk), may aid in the comprehension of charts. They also found the embellished charts to be more memorable than “plain” charts: their recall was significantly better after a two-to-three-week gap. As Cawthon and Vande Moere (2007) put it, “aesthetic should no longer be seen as a cost to utility”. The challenge seems to be in finding a balance between the technical-scientific, radical minimalism and a richer approach. The risks of minimalism are fake objectivity and neutrality, while a strong focus on aesthetics is a risk to the content, which might get overridden by the illustrative elements. (Cairo 2013a, xvi & 73; McLaughlin 2009.) Cairo (2013a, xx) sees that the first goal of infographics is not to be beautiful just for the sake of eye appeal but to be understandable first, and maybe beautiful after that. The best possible way of presenting data always depends on the target audience, and its level of expertise, expectations, and preferences. In the context of online market data visualization, there are no attractiveness- oriented executions. No illustrative elements are used. Data is offered to readers in a – at least allegedly – neutral way.
2.3.2 Scientific orientation vs. journalistic orientation
Market data visualizations in editorial contexts are located in the junction of two worlds with distinct intentions and values: those of economics (focused on content and numbers, emphasizing objectivity and raw data) and, on the other hand, journalism (focused on the reader, emphasizing viewpoints and processed information). Utility is more in the interests of economics, and attractiveness is more valued in journalism. Depending on the role and intentions of the publisher, some market data presentations are located closer to the economics and some closer to journalism. A dichotomy by Lankow et al. (2012) comes very close to that of economics and journalism. They distinguish explorative and narrative approaches to infographic design. Explorative infographics, which would correspond to economics’ approach, are typically minimalist and only include elements that represent data.
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Their main application areas are academic research, science, business intelligence and data analysis. Narrative infographics, which would correspond to journalistic approach, are illustrative and design-focused. While explorative infographics seek to communicate information in the clearest and most concise manner, narrative infographics aim at appealing to the viewer with engaging visuals, both informing and entertaining. Harold Evans (1978) writes about the same phenomenon but uses the words fact and flavour graphics. Market data visualization needs both of these approaches, for different kinds of publications, contexts, uses and users. For example, when a small-scale investor just wants to check one particular exchange rate with a smartphone while waiting for the bus, no narrative features are needed nor wanted. But when someone novice in investing is looking for information, narrative elements can be appreciated, because they make the content more approachable. And from the publisher’s point of view, if the point is to make investing and the publications’ web pages more appealing to everyone, narrative features should obviously be considered, whereas if the publication should give a very professional impression, explorative approach might be more suitable.
2.4 Quality criteria for visualizations
What kinds of criteria are there for good quality visualizations? A simple way of judging a visualization is to say that if the readers understand it, it is good, and if the readers do not understand it, it is not good – however, the message does not need to be understood at a glance (Munk 1999). The speed of reading or understanding is a criterion mentioned quite unnecessarily: one should instead evaluate how well visualizations communicate something that otherwise would have been difficult to see (Cairo 2013a, 123; citing W. S. Cleveland). The main task of the information designer is to produce visualizations that help people think and that therefore go together with the way they think. Tufte (2006) talks about analytical design, which aims at turning thinking principles into seeing principles. Therefore the role of an information designer is to anticipate the process of the brain (Cairo 2013a, 17). According to Edward Tufte (2001, 13) excellence in information graphics consists of complex ideas communicated with clarity, precision and efficiency. A good display should:
x Invite the reader to think about the substance rather than about methodology, design, production or something else x Avoid distorting what the data has to say x Present many numbers in a small space (data-density) x Make large data sets coherent x Encourage the eye to compare different pieces of data x Reveal the data at several levels of detail
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x Serve a reasonably clear purpose: description, exploration, tabulation or decoration x Be closely integrated with the statistical and verbal descriptions of a data set.
Tufte (2001, 177) further describes attractive displays of statistical information as they: x Have a properly chosen format and design x Use words, numbers and drawing together x Reflect a balance, a proportion, a sense of relevant scale x Display an accessible complexity of detail x Often have a narrative quality, a story to tell about the data x Are drawn in a professional manner, with the technical details of production done with care x Avoid content free decoration, including chart junk.
2.5 Classifying Visualizations
There are many different classifications of visualizations. Alberto Cairo distinguishes (2013a, 19) figurative (iconographs, where for example one human figure corresponds to 1000 people) and non-figurative graphics (where there is no iconic equivalence: for example a colour corresponds to a numeric value). Munk (1999) has suggested the following division into four: x Static visualization, i.e. sectional drawings x Step-by-step visualization x Visualization of numbers (i.e. pie charts) x Maps However, it is not always easy to make a distinction between the categories. For example maps very often are actually visualizations of numbers. (E.g. Järvi 2006, 59.) Ander (2003) has a practical approach, using the temporal dimension as a criterion: x Day’s graphics (very recent incidents) x Planned graphics (a few days can be used for production) x Feature graphics (they are timeless). In order for this classification to cover also market data visualizations, we claim that it would need one more category, dynamic, constantly updating graphics.
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2.5.1 Classification Frameworks
Keim's (2002) classification of information visualization techniques is based on the type of data to be visualized, the visualization technique, and the interaction and distortion technique. The data may be for example one dimensional (such as temporal data), two dimensional (such as geographical maps) or multi- dimensional. Different visualization techniques include standard 2D/3D, geometrically-transformed, iconic, dense pixel and stacked displays. We dedicate Chapter 3 to the discussion of visualization techniques for market data, covering certain data types (e.g. time-oriented data in section 3.2 and hierarchical data in section 3.5) in more detail. Lankow et al. (2012) classify infographics with their Infographic Formats Quadrant (Figure 1). The key formats for infographics are static images, interactive interfaces and motion content. The information input (x-axis in Figure 1) is typically fixed for static images and motion content, and can be fixed or dynamic for interactive interfaces. In Figure 1, the dynamicity of display update for each format is color-coded: static (red) for still images, semi-dynamic (yellow) or dynamic (green) for interactive interfaces, and dynamic for motion content. We notice that dynamicity can refer to either the input information or display update. In section 2.5.3 we review a taxonomy for dynamic data visualization (Cottam, Lumsdaine, & Weaver, 2012) that provides a more fine-grained analysis of different levels of data dynamicity.
Figure 1 Infographic Formats Quadrant (Lankow et al., 2012)
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In the domain of editorial market data visualization, the most important visualization formats are interactive-passive: data that updates in real time (e.g. exchange rates, share process, indices) but does not require user interaction. Of course static formats exist as well, with data that does not change every minute (e.g. interest rates). Interactive-active formats are increasing along with the march of technology. Because of the complexity and volume of the underlying data (and the technological possibilities and user expectations) it is getting more essential to provide users with means to interact with the data. This means being able to search for specific data, compare it to something else, and, in different ways, to actively shape the content and the way it is displayed. We discuss interaction in more detail in section 2.5.4.
2.5.2 Choice of Encoding Mechanism
According to Cleveland and McGill (1984) people use different elementary perceptual tasks to extract quantitative information from graphs. Cleveland and McGill order them according to their accuracy as follows (from most accurate to least accurate): Position along a common scale Positions along nonaligned scales Length, direction, angle Area Volume, curvature Shading, colour saturation
Bertin (1967) identified four information visualization tasks and the encoding mechanisms suitable for each of them. The tasks are: Association: How well can the elements be perceived as similar? Selection: Do the elements form families? Order: Can the elements be perceived as ordered? Quantity: Can the elements be perceived as proportional to each other? The encoding mechanisms, or retinal variables as they can also be called, are: size, value, texture, colour, orientation and shape. Spence’s (2007, 52–53) interpretation of Bertin’s guidance is visualized in Error! Reference source not found., where the most appropriate encoding mechanisms for each of the four information visualization tasks are presented. For example,
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the different size of the elements can be used to present selection (families), order and quantity (e.g. bar charts). Texture is also a multi-purpose mechanism as it can be used to present association (which elements belong together), selection (families) and order. Shape or orientation, on the other hand, can be used only to present association. Spence emphasizes, however, that the most appropriate encoding mechanism is always very dependent upon context.
Association Selection Or der Quantity
Size
Value
Texture
Colour
Orientation
Shape
Figure 2 Bertin’s guidance regarding the suitability of different encoding mechanisms for common information visualization tasks (Spence 2007, 52).
According to Mackinlay (1986), colour and size are suitable for encoding ordinal and quantitative, but not nominal data, because they will most likely be perceived to be ordered. Heer, Kong, and Agrawala (2009) encourage the use of layering as an alternative for encoding quantitative values instead of colour saturation. Layering means using several overlapping filled line charts that are distinguished from each other by their colour tone. For an example of the use of layering, see section 3.3.4. Abela (2010) distinguishes the tasks of visualizing distribution, composition, relationship and comparison and presents a chart that shows the possible visualization forms for each task.
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Figure 3 Choosing the right chart (Abela 2010).
According to Kuusela (2000, 52), the choice of the chart type should be made based on:
x Measure of the data (i.e. per cents, averages, counts) x Dimensions of the data (how many variables are there, do they have negative values) x Gradation of the variables (on what scale is the change visible – in kilograms, grams or tons?)
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x Level of measurement (nominal, ordinal, interval, ratio) x How many elements should be included and how much space is available?
2.5.3 Dynamic Data
By dynamism we mean data-driven changes in the visualizations (versus user- driven changes discussed in the next section). A viewer interprets a static visualization by identifying the correspondences between the visual representation and its underlying data. Dynamic visualizations allow updates to the underlying data, making these correspondences moving targets. Dynamics can be introduced in visualizations in many ways: by streaming data, allowing for user interaction, using animations, or shifting between subsets of data. Cottam, Lumsdaine and Weaver (2012) present a taxonomy for dynamic data visualization. They employ Bertin’s (1967) decomposition of the visual space into two groups: spatial and retinal variables. Spatial and retinal variables are the basic building blocks of data visualization. Spatial variables determine the position, i.e. coordinates in space, of visual elements. They may be dynamic in two ways: changing existing values and changing the number of visual elements (adding or deleting). These two general types of change can be expressed as four general categories: Fixed: The spatial dimensions do not change and the number of visual elements is fixed at the start of the visualization. Visual elements do not move. Mutable: The number of elements remains fixed, but the location of elements may change over time. (Destructive updates are referred to as a “mutation” of that entity.) Create: New elements may be created in response to incoming data. Existing element positions may be mutated. Create & Delete: Elements may be created or deleted in response to incoming data. Mutability is implicit in this category. Retinal dimensions (i.e. size, value, texture, colour, saturation and shape) determine all other visual aspects. Retinal values change over time as the attributes they are associated with do. The categories of retinal dimension dynamics, in increasing order of complexity, are: Immutable: Retinal variables are unchanged. Known Scale: The scale is established when the visualization is initialized. The scale remains fixed, but future data may change the retinal presentation of existing elements. Extreme Bin: The scale is a known scale with sentinel categories (usually at the endpoints). Values outside of the regular range are assigned to a top or bottom catch-all “bin” (i.e. “100+”, “less than 0” or “No Data”).
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Mutable Scale: Updates may change the representation of an element and the mapping function itself. Scales may grow or shrink dynamically to accommodate the data being visualized.
Together retinal and spatial categories produce a matrix of 16 technique categories (Table 2) and three identity groups: preserving, transitional and immediate (see 4).
Figure 4 Taxonomy of dynamic data visualization (Cottam et al., 2012).
Table 2 Taxonomy of dynamic data visualization (Cottam et al., 2012).
Retinal IMMUTABLE KNOWN SCALE EXTREME BINS MUTABLE SCALE Spatial FIXED No dynamics Elements are Retinal encodings Retinal encodings Constant present statically with details in a where the value and number of positioned with limited range of the value’s scale statically dynamic retinal the data. convey dynamic positioned encodings of a information. elements fixed scale MUTATE Moving Moving elements Moving elements A fixed number of Constant elements with whose with emphasized moving elements, number of constant appearance may retinal range. whose retinal moving appearance change in a priori encodings and elements known ways. scales are dynamic CREATE Elements may Created elements Element creation Number of elements New elements be created but are always with emphasized can increase, their may be created their retained, but their retinal range. appearance and existing appearance is appearance may (including the ones mutated constant change over time. scales) changes with data. CREATE & The set of Elements appear Elements appear Quantity, retinal DELETE elements can and disappear; and disappear, encoding, and Elements may grow or shrink, existing elements with representa- scales can all be added, but their can change tion providing change deleted and appearance is appearance details within a mutated fixed. range.
Cottam & al (2012, 200) use as an example one illustrative visualization of finances – the Map of the market by Market Watch (figure 5) – which is deemed
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effective for monitoring market status. It is a tree-map visualization (see 3.5.1 Treemaps), where each leaf tree cell represents a stock and the stocks included are fixed. Cell size represents current price as percentage of the total of all included stocks. Colouring represents percentage change. The cells are rearranged as values change (i.e., it mutates their location and size). The cell colouring range is adjusted to accommodate the largest percentage change present. The positional changing and mutable scales indicate that this visualization belongs in the mutate – mutable scale category. Visualizations in this category belong to the Immediate identity group. Current state information can be acquired at a glance by examining the current colour scale and overall hue/saturation of a region. Comparison across time requires referral to the range of the colour scale and potentially shifting correlating positions.
Figure 5 Market Watch’s Map of the Market (pictured here in part), http://www.marketwatch.com/tools/stockresearch/marketmap, 23 September 2013.
2.5.4 Interaction
By interaction we refer to user-driven changes in the visualizations. Weber & Rall (2012, 3) define interactivity as a communication process: “it is the extent to which messages in a sequence relate to each other, and especially the extent to which later messages recount the relatedness of earlier messages.” Interaction means dialogue between the user and the visualization. The lowest level of interactivity includes “object interactivity” and linear interactivity: objects (such as buttons, people, or things) are activated with a mouse or other pointing device, and there is some form of audio-visual response. A high level of interactivity is reached, when users can influence the content or choose their own navigation path through the information graphic (“construct interactivity”). (Sims 1997.)
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Segel & Heer (2010) divide interactive visualizations into author-driven and reader-driven, depending on whether the reading path is dictated by the production or if the reader can choose it without restrictions or among certain alternatives. Author-driven implies linear ordering of scenes, clear messaging (such as repetition of key points, introductory texts, and final summaries and syntheses) and no interactivity. Reader-driven visualizations on the other hand imply non-linearity, no messaging and high interactivity. According to Segel & Heer (2010), most visualizations lie along a spectrum between these two extremes, and often combine features of both author- and reader-driven forms.
Table 3: Author-driven vs. reader-driven visualizations (Segel & Heer 2010).
Author-Driven Reader-Driven Linear ordering of scenes No prescribed ordering Heavy messaging No messaging No interactivity Free interactivity
There are both author- and reader-driven realizations in market data. One could however hope that the amount of interaction would increase. Only interaction will make it possible to take full advantage of those enormous amounts of data that exist in finances – only through interaction can each individual small-scale investor’s or entrepreneur’s needs be met. Static presentations do not quite suit the online environment. However there are technical challenges with interaction and the different devices. In the spring of 2013 we benchmarked the current ways of presenting market data and also tested the existing presentation on different platforms. It turned out that 56 per cent of the web sites we tested (and they were big, well know services by big publishers) had interactive elements that could not be used on tablets (because they use Flash or Java). Only 44 per cent could be used with tablets (but they might have problems on personal computers that use older versions of Internet Explorer). Spence (2007) identifies three interaction modes: continuous, stepped, and passive. In continuous interaction the results of the user’s actions (e.g. moving the mouse or running a finger along the surface of a tablet display) are updated and presented to the user in real-time or with such a negligible delay that the user experiences the interaction as continuous. In stepped interaction a single simple action (e.g. a mouse click, a keyboard press, or a tap on a tablet device) causes a change in the visualization. During passive interaction (which may also be called visual or sensory interaction) the user does not take visible action, but derives insight from a visualization just by looking at it. According to Spence (2007), the most useful visualization tools use a mixture of all three modes, which he calls composite interaction. Based on our benchmarking study, continuous interaction on market data websites is still quite rare on tablet devices. If interactivity functions, it is mainly stepped. Heer and Shneiderman (2012) present a more detailed taxonomy of interactive dynamics (see Table 2) with 12 tasks grouped into three high-level categories: Data & View Specification (visualize, filter, sort, derive), View Manipulation (select, navigate, coordinate, organize), and Process & Provenance (record, annotate, share, guide).
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Overall, market data visualizations are quite elementary, and interactivity in them is not very multidimensional. Tables are a central way of presenting information. Users are often able to sort the data in the tables, thereby influencing the way in which it presented. Their options, however, are limited to choosing which columns appear in which order (the way data is shown) they hardly ever can choose what data they want to appear in the table. Out of Heer & Shneiderman’s (2012) taxonomy, the third group, process & provenance, hardly exists with the current market data visualizations we examined. Interactivity means mainly manipulating the view and specifying data and view. The most common, or central interactive element is usually a chart that presents one particular stock’s value over a certain time period. Often one can choose one or several other stocks or indices that the stock’s value is compared to.
Table 2 Taxonomy of interactive dynamics (Heer & Shneiderman, 2012)
Data & View VISUALIZE data by choosing visual encodings. Specification FILTER out data to focus on relevant items. SORT items to expose patterns. DERIVE values or models from source data.
View SELECT items to highlight, filter, or manipulate them. Manipulation NAVIGATE to examine high-level patterns and low-level detail. COORDINATE views for linked, multi-dimensional exploration. ORGANIZE multiple windows and workspaces.
Process & RECORD analysis histories for revisitation, review and sharing. Provenance ANNOTATE patterns to document findings. SHARE views and annotations to enable collaboration. GUIDE users through analysis tasks or stories.
2.6 Fourfold table of market data visualizations
Based on previous literature and benchmarking of the visualizations of stock market data (carried out in the spring of 2013), we suggest a fourfold table model for classification. One-dimensional classifications are too limited for the purpose. Drawing on Cairo (2013a) and Segel & Heer (2010), our y-axis varies according to the content and the message: is the way it is read up to the author or the reader? Is the message just presented to the reader, or does the reader have the opportunity to explore it herself? X-axis on the other hand varies according to the way the content is designed: is the focus on user-friendliness or on the data, based on the two orientations or genres that affect market data visualizations.
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Figure 6 Fourfold table about the visualization of market data.
Map of the market, which was already mentioned in section 2.5.3 Dynamic Data, is an example of a reader-driven and user-oriented visualization. Journalistic orientation means that the data is offered for observation from some viewpoint, not all data is available. In Map of the market the user can explore the different industries and companies. The data is limited, compared to the next one. Content-oriented and reader-driven visualizations are ones that have interactive features and are usually based on a large amount of data. Scientific orientations are usually recognizable by their visuality, which is more technical and detailed than in journalistically oriented visualizations. A visualization of this kind is more like a user interface to the data (here the example is from the web site of the OP- Pohjola Bank). Content-oriented and author-driven visualizations are mainly static, or the interactivity is limited. In the example table from the German newspaper Handelsblatt the reader can choose, for example, according to which column the rows are arranged. User-oriented and author-driven visualizations are often static, non-interactive. The amount of data is limited, and the point of view is clear. These visualizations are quick to read, as is the bar chart by Dagens Næringsliv.
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3. Visualization Techniques for Market Data
In this section we present visualization techniques that can be used for market data presentations.
3.1 Basic Techniques
3.1.1 Pie Charts
Pie charts (named so because of their resemblance to a sliced pie) are circular graphs divided into sectors, displaying numerical proportions, for making part-to- whole comparisons. A pie chart encodes quantities by the two-dimensional areas of its sectors (slices) and the angles formed by the slices. Based on section 2.5.2 of this report we know that these are less efficient perceptual encoding mechanisms than position (i.e. 2D location). This makes it difficult to compare both different sectors of a single pie chart and data between several pie charts (Few, 2007). Pie charts are often opposed by professionals because of their ineffectiveness compared to bar charts (see Section 3.1.2) and their ambiguity: people read them in different ways (some evaluate the angle, some the surface and others the length of circumference), they don’t have a shared horizon or X-axis, and also the order of the variables is more difficult to discern (i.e.Tufte 2001, 178; Cairo 2013a). Pie charts should be used only when there are no more than a few subcategories. Tufte (2011, 178) even writes that “pie charts should never be used”. However, according to Lankow et al. (2012) the value of pie charts is in that they can communicate big ideas quickly. Also, without a doubt most people are used to them and feel comfortable reading them. In market data visualizations pie charts are barely ever used. Below is one example from the Spanish newspaper Expansion’s website, and, as comparison, the same data as a bar chart. One can hardly disagree on the latter being more an illustrative and efficient visualization.
Figure 7 Pie chart (http://www.expansion.com/mercados/micartera.html?cid=BA18001&s_kw=Mi-cartera-etools)
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Santander 33,05 Apple 19,86 Repsol 12,96 Barclays Bank 12,13 Iberdrola 11,11 Bestinver Internacional 6,32 Renta 4 Nexus 4,58 0 10 20 30 40
Figure 8 The same data in a bar chart for comparison
3.1.2 Bar Charts
Cartesian charts, where elements are ordered along a common, straight x-axis, have been found to be more powerful (i.e. perceptually efficient) than polar charts (Hofmann, Follett, Majumder, & Cook, 2012), and bar charts (in comparison with pie charts) are no exception. Lankow et al. (2012) call them “the most straightforward and versatile of all graph types”; they can be used to visualize nominal comparison, time series, ranking and part-to-whole relationships. In a bar chart the length of each bar (or column in a vertical bar chart) is proportional to the value it represents. Error! Reference source not found. shows an example of a horizontal bar chart from Dagens Næringsliv.
Figure 9 The “Sector barometer”, a horizontal bar chart displaying the rate of change per industrial sector, Dagens Næringsliv (http://www.dn.no/finans/portal/marketOverview- nordic)
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Grouped and stacked bar charts enable more complex data comparisons. Stacked bar charts are useful for displaying multiple part-to-whole relationships, such as in Figure 10, which visualizes the recommendations of several investment analysts regarding Nokia Oyj stocks at different points in time.
Figure 10 Stacked bar chart visualizing the recommendations of 41 investment analysts regarding Nokia Oyj stocks, Financial Times (http://markets.ft.com/research/Markets/Tearsheets/Forecasts?s=NOK1V:HEX)
3.2 Visualizing Time-Oriented Data
The majority of market data that constantly piles up is time-oriented: development of the value of stocks, indices, and interests. Line charts are commonly used for visualizing them. In this section, we examine a few other alternatives as well.
3.2.1 Line Charts
Line plots or line charts are the most commonly used visual representation of time-oriented data. They are used for continuous data (bar charts are more suitable for discrete data). Line charts allow users to identify both values at specific points in time, and trends. Stock price data is therefore commonly visualized using line charts. An example of a typical stock line (or “mountain”) chart is shown in Figure 11.
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Figure 11 Line chart visualization of the Apple Inc stock, Reuters.com (http://www.reuters.com/finance/stocks/chart?symbol=AAPL.O)
3.2.2 Open-high-low-close (OHLC) charts
For stock data visualization, open-high-low-close (OHLC) charts are a common alternative to line charts. OHLC charts are typically used to visualize price movements of financial instruments. In the example chart shown in Figure , the length of each vertical line represents the stock price range during one day. The tick marks on the vertical line show the opening (on the left side of the vertical line) and closing (on the right side of the vertical line) prices of the day.
Figure 12 OHLC chart of the Apple Inc stock, Reuters.com (http://www.reuters.com/finance/stocks/chart?symbol=AAPL.O)
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3.2.3 Candlestick Charts
Candlestick chart is a combination of line and bar charts used to display market price data. Just like the OHLC chart, it shows the opening, high, low and closing prices. Figure 13 shows an example of a color-coded candlestick chart from Reuters.com. In the chart a higher (as compared to the previous day) closing price is marked with green and a lower closing price is marked with red. If the day’s closing price is lower than opening price, the rectangle is filled (red or green); otherwise it is white.
Figure 13 Candlestick Red/Green chart of the Apple Inc stock, Reuters.com (http://www.reuters.com/finance/stocks/chart?symbol=AAPL.O)
3.2.4 Index Charts
Multivariate time-series data can be visualized efficiently using indexing, a method that was introduced by Bertin (1967), and empirically proved effective and efficient by Aigner and Kainz (2011). Index charts are interactive line charts that show percentage changes for time-series data based on an index point. The users of stock market data are most likely not as interested in absolute stock price values as they are in the evolution of the values over time (e.g. how a company performs now compared to one month ago) or when comparing to other values (e.g. how a company performs today in comparison with other companies) (Vande Moere, 2004). Indexing is therefore very commonly used in line charts comparing the price development of two or more stocks; an example from Bloomberg.com is shown in Figure . The chart displays the relative (percentage) change in values in relation to the index point (June 6, 2012). Indexing makes it possible to place values of largely different magnitudes in the same space (for example in 14, at the point of interest, on September 28, 2012, the value of the Microsoft stock is $29.76, and the value of the Apple stock is $667.105).
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Figure 14 Index chart comparing the development of Microsoft and Apple’s stock prices during the last year (Source: Bloomberg, http://www.bloomberg.com/quote/MSFT:US/chart).
Figure shows an example of an index chart (Heer, Bostock, & Ogievetsky, 2010) with the selected index point marked with a red vertical line. The chart shows the relative magnitude of gains or losses if money was invested during the selected reference month, in this case in September 2004.
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Figure 15 Index Chart of Selected Technology Stocks, 2000-2010 (Heer et al. (2010, data source: Yahoo! Finance), see the interactive version at: http://hci.stanford.edu/jheer/files/zoo/ex/time/index-chart.html
3.3 Space-Economical Visualizations
Edward Tufte (2001, 2006) has for a long time been known as a strong advocate of data-density and effective use of ink and space. Currently, with the small screens of mobile devices, this objective is all the more relevant. Space- economical visualizations also make it possible to bring visualizations into website elements where there have in the past not been any, such as tables.
3.3.1 Small Multiples
Coordinated multiple views that enable efficient comparisons can be achieved with the use of small multiples, advocated by Edward Tufte (2001). Small multiples are an alternative for stacked graphs (where multiple series are placed on top of each other in the same chart, possibly producing overlapping curves and thereby reducing legibility (Heer et al., 2010)). Figure depicts an example of the use of small multiples to visualize the development of various currencies and stocks. Small multiples can be used to give an efficient overview, enabling the users to get an idea of the state of many objects of their interest (whether they are stocks, currencies or indices) at a quick glance.
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Figure 10 Small multiples in MarketWatch (http://www.marketwatch.com/myportfolio)
3.3.2 Sparklines
Small multiples can be used with sparklines, “small high-resolution graphics usually embedded in a full context of words, numbers and images. Sparklines are datawords: data-intense design-simple, word-sized graphics.” (Tufte 2001, 171.) They track and compare changes over time, show overall trends along with local detail and as such, have obvious application for financial and economic data (Tufte 2001, 173). Examples of the use of sparklines on market data websites are shown in Figure and Figure . The obvious advantage of sparklines is their small size which makes it possible to include them for example in data tables, making them perceptually more efficient.
Figure 11 Stock quotes visualized with bar chart sparklines in Bloomberg Businessweek (http://www.businessweek.com/markets-and-finance)
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Figure 12 Key currencies visualized with line graph sparklines in Market Watch (http://www.marketwatch.com/investing/currencies)
Sparkline visualizations can be created for the web easily for example using jQuery Sparklines6, a jQuery plugin for generating sparklines directly in the browser.
3.3.3 Pixel Bar Charts
Pixel bar charts (Keim, Hao, Ladisch, & Dayal, 2001) transform line graphs into one-dimensional bars, using colour to code the values. Pixel bar charts require less vertical space than line charts (see Section 3.2.1) to display large differences in values, so they enable efficient visualization of stock price volatility (Ziegler et al., 2010). As their downside, colour is a less accurate indicator of value than position, so small changes are not as easily perceivable as in a line graph. Because of this, Ziegler et al. colour-code the relative percentage changes of the values (instead of absolute values). For an example, see Figure .
6 http://omnipotent.net/jquery.sparkline
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Figure 13 Visualizations of the Ameris Bancorp share, from the top: line chart of absolute value; bar chart of relative percentage change; colour-coded pixel bar chart reflecting relative percentage change and price volatility; pixel bar chart averaged over each three pixels (Ziegler et al., 2010).
3.3.4 Horizon Graphs
Horizon graphs (Reijner, 2008) are another technique for increasing the data density of time series graphs while preserving resolution. The data density of a filled line chart (Figure a) can be doubled once by mirroring negative values (distinguished by colour-coding) into the same space as positive values (Figure b), and again by dividing the graph into bands (distinguished by colour darkness) and layering them to create a nested form (Figure c). It is possible to use more than two bands, further increasing data density.
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Figure 20 (a) Filled line chart, (b) Mirrored chart, (c) 2-band horizon graph (Heer, Kong, & Agrawala, 2009)
It may take some time to learn to read horizon graphs, but especially with small chart sizes they have been found to be more effective than the standard line chart (Heer et al. 2010). However, Heer et al. (2009) found that value estimation accuracy for 4-band charts is lower than for 2- and 3-band charts. Also, estimation time was found to increase with each additional band. Mirroring alone does not impair graphical perception: 1-band mirrored charts were perceived at equal or better speed and accuracy than regular line charts. In summary, for larger chart sizes it is advisable to use single 1-band mirrored charts and for small chart sizes to add another band. For a web-based interactive example (with source code) of a horizon graph implemented by Mike Bostock (using a layout algorithm based on Heer et al.'s (2009) work) in D3, see: http://bl.ocks.org/mbostock/1483226. Heer et al. (2009) present an alternative approach to the mirrored horizon graph. They call it the offset graph. In offset graphs negative values are not reflected but instead offset so that the zero point for the negative values is at the top of the chart. In terms of value estimation time and accuracy, Heer et al. found offset graphs to be comparable to mirrored graphs.
Figure 21: Offset graphs with 2, 3, and 4 bands. (Heer et al. 2009, 1306)
Perin et al. (2013) introduce interactive horizon graphs (IHG), a technique for exploring multiple time series. IHG is a unification of reduced line charts and horizon graphs that uses a combination of pan and zoom for interaction. For a dynamic implementation in D3, see: http://www.aviz.fr/Research/IHG.
3.4 Overview Visualizations
3.4.1 Scatter Plots
A scatter plot displays the values of two variables of each data element. For instance the scatter plot in Figure 22 displays stock price and its percentage change. The plot in Figure 22 shows an additional third variable, time, which is encoded with colour.
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Figure 22 A scatter plot displaying stock price and its percentage change. The colour of each dot indicates time: the darker the earlier. (Lei & Zhang, 2010)
3.4.2 Heatmaps
Heatmaps are visualizations where the values in a matrix are represented as colours. Heatmaps are used in many application areas, and can be implemented at various resolutions using many different colour schemes. In financial data visualization heatmaps can provide good overviews, for example, as seen in Figure 23, of the current situation of different stock indices.
Figure 23 A heatmap visualization of the Dow Jones Industrial Average stock market index by De Tijd (http://www.tijd.be/geld_en_beleggen)
3.5 Visualizing hierarchical data
The standard library of charts and graphs is not helpful with hierarchical, multi- level data, where categories have subcategories and a change in one data point has a big effect on surrounding data. In this section we examine two alternative visualizations for such data.
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3.5.1 Treemaps
Treemaps (Johnson & Shneiderman, 1991) are an example of a stacked display technique (Keim, 2002). They are used widely for visualizing hierarchies (Schulz et al., 2011). A treemap maps the full data onto a rectangular region in a hierarchical manner, partitioning it into smaller rectangles and utilizing 100 percent of the available space. The “Map of the Market” visualization (see Figure ) on Wall Street Journal’s Smart Money website is probably the best known hierarchical visualization of financial data. It visualizes stock market activity organized by industry sectors. Each leaf tree cell (small rectangle) shows the current situation of an individual company’s stock. The size of the cell represents the company’s market value (the value of its stock price as percentage of the total of all stocks included in the map). The colour of the cell is determined by the current percentage change of the stock price.
Figure 24 Smart Money’s Map of the Market (http://www.smartmoney.com/map-of-the- market/); a treemap visualization of stock market activity organized by industry sectors.
The main shortcoming of treemaps is that they can only show a snapshot of the situation at one single time interval (Ziegler, Jenny, Gruse, & Keim, 2010).
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3.5.2 Sunbursts
A sunburst (Stasko, Catrambone, Guzdial, & Mcdonald, 2000) is a visualization method similar to a treemap, but it uses a radial layout instead of a rectangular one. Elements in a hierarchy are laid out radially, with the top of the hierarchy at the center. The angle swept out by an item and its colour correspond to some attribute of the data. Stasko et al. found sunbursts to outperform treemaps in terms of conveying structure and hierarchy, as well as in general user preference. An example of a simple sunburst visualization of market data is shown in Figure . Treemaps did not score very high on aesthetics in Cawthon and Vande Moere's (2007) study, while the sunburst technique received the highest aesthetic scores among the 11 evaluated techniques. On the other hand, in Jiang, Webber, and Herbert's (2005) experiment with a minority game, treemaps were better than sunbursts for comparing the capitals of agents. Sunburst has been patented by Oracle for visualization and interaction with financial data7.
Figure 25 Sunburst visualization in the Bloomberg iPad app
3.6 Visualization Aids
Kong and Agrawala (2012) introduce graphical overlays: visual elements layered onto charts to facilitate a larger set of chart reading tasks. Kong and Agrawala identify five types of overlays on the basis of what they provide: 1. Reference structures (e.g. gridlines) to aid the viewer in extracting and comparing values 2. Highlights (e.g. outlines around important marks) to draw attention to certain data points or areas 3. Redundant encodings (e.g. numerical data labels) to allow the extraction of numerical data in multiple ways
7 http://www.google.com/patents/US20120240064
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4. Summary statistics (e.g. mean, max) to depict aggregate information 5. Annotations (e.g. descriptive text) to help viewers communicate and collaboratively analyze charts While all overlays facilitate chart reading, they also increase visual clutter, so one should exercise caution when adding them. In interactive visualizations it is useful to provide the users with the possibility to choose which overlays they want to see, and the means to easily toggle their visibility. Typical overlays in stock data charts are for example different moving averages, price channel (two parallel trend lines that form a pattern for the stock), Bollinger Bands (a price volatility indicator), Parabolic Stop and Reverse (a method for finding potential reversals in the price direction), volume by price and stock splits. It is also common to annotate stock charts with different events that might have an influence on the stock price, e.g. news, key developments, splits, dividends and earnings.
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4. Methods for evaluating Information Visualization
In this section we discuss three common methods for evaluating information visualizations: heuristic evaluation, user tests and eye-tracking. The heuristic evaluation and user test carried out in this project are described in the forthcoming report8.
4.1 Heuristic Evaluation
Heuristic evaluation is a quick and cost-effective usability inspection method that can be applied to any stage of user interface development. Heuristics are used widely in the field of human-computer-interaction to evaluate usability, and recently they have been recognized also within the information visualization community. Forsell (2012) provides recommendations on how to apply heuristic evaluation to information visualization evaluation. The evaluators are typically usability experts, but also users and domain experts can be used to identify different types or problems (Petrie & Power, 2012). They inspect the user interface (or other object of evaluation) and evaluate how well it conforms to a set of principles, or heuristics. The purpose of the evaluation is typically to find usability problems, which are described (including a mention of which heuristic(s) they violate) and reported along with an evaluation of their severity. Zuk and Carpendale's (2006) selection of perceptual and cognitive heuristics based on theories from Bertin (1967), Tufte (2001) and Ware (2004) is an example of heuristics developed specifically for information visualizations. Forsell (2012) provides a review of other existing heuristics.
4.2 User Tests
Visualizations are commonly evaluated in the manner of typical usability evaluations, by having test participants carry out predetermined tasks with the visualization and measuring their performance with metrics such as speed and error rate (e.g. Aigner & Kainz, 2011; Cleveland & McGill, 1984; Goldberg & Helfman, 2011; Heer et al., 2009; Hofmann et al., 2012; Merino et al., 2006; Perin et al., 2013). In this way, different visualization techniques can be compared. The participants can also subjectively assess the visualization, i.e. express their preference and other opinions. For example, Merino et al. (2006) have three sections in their online (subjective) visualization evaluation: background questionnaire (for collecting information about the participant), evaluation tasks (questions to be answered after observing the visualization), and a feedback questionnaire (asking for the participants’
8 1.2.3.4 Development of market data presentations, published later in 2013.
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opinion regarding the visualization and the evaluation). The actions and tasks used in their evaluation task (of visualization techniques for historical and real-time stock data) are listed in Table 3. In addition, as objective performance measures, Merino et al. (2006) measured the time spent with each task and the ratio of correctly completed tasks.
Table 3 Actions and tasks for evaluating stock data visualization techniques (Merino et al. (2006)
Type of Task Action Identify Identify the best period of the stock ”T” (AT&T Corp.) Locate Locate the stock ”IBM” and the approx. price it reached in May 1999 Select the stock or stocks you would invest in right after the displayed period, if Distinguish you had $3000 Categorize Do you see different classifications in the displayed data? Cluster Choose two stocks that showed a similar behavior during the same period Rank How are the different stocks in the image ordered? Compare the performance of the stocks ”KO” (Coca-Cola) and ”MRK” Compare (Merck) Associate When did most of the stocks reach their highest price in their history? Could the performance of Intel have had an influence on the performance of Correlate Microsoft?
Since traditional user tests consume a lot of resources, crowdsourcing presents an attractive alternative for visualization evaluations. It has been found to provide high-quality data, making it a viable option for web-based evaluations, i.e. anything that it not dependent on a specific physical or environmental context (Heer & Bostock, 2010; Hofmann et al., 2012).
4.3 Eye Tracking
Goldberg and Helfman (2011) discuss the suitability of eye tracking as a methodology for visualization evaluation. The strength of eye tracking lies in its ability to provide more specific data and thereby a deeper understanding (if compared to metrics such as task completion time and accuracy) of how people read information visualizations (Calitz, Pretorius, & Greunen, 2009). Eye tracking measures for information visualization evaluation identified by Calitz et al. (2009) are listed in Table 4.
Table 4 Eye tracking measures (Calitz et al., 2009)
Eye Tracking Measure Meaning/use Number of fixations Number of fixations is negatively correlated with search efficiency. Fixation duration Longer fixation durations imply complexity and difficulty of a display
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Number of fixations on Indication of the importance of a system element each area of interest Number of gazes on each Indication of the importance of a system element area of interest Scan path Indication of the efficiency of the arrangement of elements in the user interface Time to the first fixation Useful for identifying how easy it is to find a on the target area of specific search target interest
One of the major drawbacks of eye tracking is that the data analysis is far from trivial. Even though most commercial eye tracker manufacturers provide analysis software that enables certain types of results visualizations very easily, more advanced analysis requires deep understanding of the nature of the data and is subject to error and misinterpretation. Some of the challenges include defining gaze fixations and assigning them to areas of interest (AOI), choosing appropriate metrics and addressing potential gaze location errors (Goldberg & Helfman, 2010). For a comprehensive overview of eye tracking methodology, ranging from theoretical discussion to practical recommendations, see Holmqvist et al. (2011).
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5. Tools for Visualizing Market Data
Until now the best way to create interactive (and aesthetically pleasing) interfaces for the visualization of market data has been to use Adobe Flash. Flash (as well as Java) is still commonly used for advanced interactive stock charts. In our market data website benchmark survey, 56% of the reviewed 44 websites used Flash or Java. These days, however, a constantly increasing number of people are using tablet devices and smart phones to access market data sites. A large number of these devices are Apple products that do not support Flash. Also, with the coming of HTML5, Adobe discontinued its development of Flash for mobile browsers in 2011 (Lankow et al., 2012). This means that Flash- (or Java-, for that matter) based visualizations are unusable for tablet and smart phone users. These developments make it vital to find other techniques for visualizing market data. Nowadays it is possible to create advanced (in terms of interaction, dynamics and aesthetics) visualizations using JavaScript visualization libraries instead of Flash or Java. In the following, we present ten different tools (some of them commercial products, others open source approaches) that can be used to create different types of market data visualizations. The list is by no means exhaustive, but meant as a quick review of some of the possible solutions available in 2013. Four of the tools are open source JavaScript libraries that can be freely used by anyone. There exist several other such libraries, not discussed in this report, for example gRaphäel9 and JScharts10. Professional developers can employ a combination of different tools to suite their various needs. The tools are presented briefly by describing the techniques they employ and the visualization types they can be used to produce. We also provide licensing information, browser compatibility lists (largely for the purpose of evaluating the tools’ suitability for mobile development) and visualization examples.
5.1 AmCharts
AmCharts provides tools for creating stock charts and dozens of other types of interactive JavaScript graphs. The JavaScript Stock Chart (Figure and Figure 14) is meant for visualizing financial data in particular.
9 http://g.raphaeljs.com/ 10 http://www.jscharts.com/
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Figure 26 JavaScript Stock Chart for stock price comparisons (http://www.amcharts.com/stock-chart/)
Figure 14 Stock Events Chart (http://www.amcharts.com/stock-chart/stock-events/)
Website: http://www.amcharts.com/
Browser compatibility: Modern browsers: Firefox 3.6+, Chrome 14+, Safari 4.0+, Android 3.0+, iOS (all versions), IE9+. For IE 6-8 SVG is replaced with VML. Demos/examples: http://www.amcharts.com/stock-chart/ http://www.amcharts.com/javascript-charts/ Licensing: http://shop.amcharts.com/#jscharts:1
5.2 AnyChart
AnyChart is a Flash/JavaScript (HTML5) based solution for over 30 types of visualizations (see Figure for an example of a stock chart and Figure for a
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horizontal linear gauge). AnyStock is meant for visualizing market data, but it is currently Flash based. An HTML5 based version of AnyStock will possibly be available during 2013.
Figure 28 Stock Chart (http://anychart.com/products/anychart/gallery/samples/ACME- Corp-Stock-Prices.html#html-view)
Figure 29 Horizontal Linear Gauge (http://www.anychart.com/products/anychart/gallery/samples/Horizontal-Linear-Gauge- Multiple-Color-Ranges.html#html-view)
Website: http://www.anychart.com
Browser compatibility: Anychart 6.0 (HTML5) works on all modern browsers and mobile platforms (Android 2.2+, iOS) Demos/examples: http://www.anychart.com/products/anychart/gallery/
Licensing: http://www.anychart.com/buy/
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5.3 D3
D3 (Data-Driven Documents) (Bostock, Ogievetsky, & Heer, 2011) is a JavaScript library for the dynamic visualization of data for the web. It uses HTML, SVG and CSS and is not tied to any proprietary framework. Figure 0 and Figure show examples of interactive graphics implemented using D3. There are also plugins for D3, for example Cubism.js (http://square.github.io/cubism/) for visualizing time series using horizon graphs. See the D3 Show Reel at: http://vimeo.com/29862153
Figure 30 Zoomable Treemap (http://mbostock.github.io/d3/talk/20111018/treemap.html)
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Figure 31 Infographics in The New York Times implemented using D3 (http://www.nytimes.com/interactive/2013/04/08/business/global/asia-map.html)
Website: http://d3js.org/
Browser compatibility: Works with modern browsers: Firefox, Chrome, Safari, Opera, and Internet Explorer 9+. D3 uses SVG to present graphics, so it is not compatible with older versions of IE. Demos/examples: https://github.com/mbostock/d3/wiki/Gallery
Licensing: Open source, published under the BSD license
5.4 Envision.js
Envision.js is a JavaScript library for dynamic and interactive HTML5 visualization. Figure shows an example of a financial chart created with it. Envision.js utilizes touch gestures, providing a smooth interaction for tablet users.
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Figure 32 HTML5 Finance Template (http://www.humblesoftware.com/envision/demos/finance)
Website: http://www.humblesoftware.com/envision/
Browser compatibility: Modern browsers, IE 6+; touch/mobile support
Demos/examples: http://www.humblesoftware.com/finance/index
Licensing: Open source, available at: http://github.com/HumbleSoftware/envisionjs
5.5 Flot
Flot is a JavaScript plotting library for jQuery. Flot emphasizes simplicity of use, attractiveness and interactivity. It can be used to create simple interactive line, bar and pie charts. Figure shows an interactive Flot line chart with a vertical crosshair.
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Figure 33 An interactive chart with a vertical crosshair (http://www.flotcharts.org/flot/examples/tracking/index.html)
Website: http://www.flotcharts.org/
Browser compatibility: IE 6+ (for versions before IE 9 employs the HTML5 canvas emulator Excanvas), Chrome, Firefox 2+, Safari 3+, Opera 9.5+, iOS (but does not seem to fully utilize touch gestures) Demos/examples: http://www.flotcharts.org/flot/examples/
Licensing: Open source
5.6 FusionCharts
FusionCharts provides several different data visualization products of which PowerCharts XT is suitable for visualizing stock data.
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Figure 34 Interactive Candlestick Chart (http://www.fusioncharts.com/demos/gallery/#candlestick-chart)
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Figure 35 Heat Map Chart for Conversion Rates (the pointers in the gradient scale can be dragged to see data sets lying in a selected range only) (http://www.fusioncharts.com/demos/gallery/#heat-map-chart)
Website: http://www.fusioncharts.com/
Browser compatibility: Can use either Flash or JavaScript; works on desktop and tablet/mobile devices Demos/examples: http://www.fusioncharts.com/demos/gallery > PowerCharts XT Licensing: http://www.fusioncharts.com/buy/#!website-apps
5.7 Highcharts
Highcharts provides two different tools for the creation of interactive JavaScript/HTML5 charts: Highcharts JS is a generic charting library, and Highstock JS is meant specifically for visualizing stock data (for an example stock chart see Figure ).
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Figure 36 Apple Inc Stock Chart implemented using Highstock (http://www.highcharts.com/stock/demo/line-markers)
Website: http://www.highcharts.com/
Browser compatibility: Modern browsers, IE 6+, iOS
Demos/examples: Highcharts JS: http://www.highcharts.com/demo/ Highstock JS: http://www.highcharts.com/stock/demo/ Licensing: Highcharts JS: http://shop.highsoft.com/highcharts.html Highstock JS: http://shop.highsoft.com/highstock.html
5.8 jqPlot
jqPlot is a plotting and charting plugin for jQuery. It can be used to create simple, interactive line, bar and pie charts. Figure depicts an OHLC chart with a mouse over (or tap on tablet devices) feature (showing the exact OHLC values for a specific date) created with jqPlot.
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Figure 37 OHLC Chart (http://www.jqplot.com/tests/candlestick-charts.php)
Website: http://www.jqplot.com/
Browser compatibility: Has been tested on the following browsers: IE7&8, Firefox, Safari, Opera. Functions on tablet devices, but does not fully utilize the touch properties. Demos/examples: http://www.jqplot.com/tests/
Licensing: Open source
5.9 Tableau
Tableau is a commercial desktop application for visualizing and presenting data. The graphical user interface makes it easy to use in comparison with most of the other reviewed tools that require scripting. Figure shows a hierarchical time series visualization of gross domestic product in the different regions through time, and Figure is a candlestick chart of the Coca Cola stock.
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Figure 38 “World GDP Through Time” (http://www.tableausoftware.com/learn/gallery/world-gdp-and-business)
Figure 39 Candlestick Chart of the Coca Cola stock (http://www.tableausoftware.com/learn/gallery/coke-stock-price)
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Website: http://www.tableausoftware.com/products/public
Browser compatibility: Optimized for touch displays
Demos/examples: http://www.tableausoftware.com/learn/gallery http://www.tableausoftware.com/public/gallery Licensing: http://www.tableausoftware.com/products/desktop/specs
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6. Conclusions
Based on our benchmarking study of market data visualizations11 our overall impression is that there are surprisingly few innovative and interactive visualizations and that the majority of visualizations are quite simple. In the concluding section of this report we reflect on possible reasons for this simplicity and present our aspirations for the future development in the field.
6.1 Rational-scientific approach
Market data visualizations in editorial contexts are located in the junction of economics (focused on content and numbers, emphasizing objectivity and raw data) and, on the other hand, journalism (focused on the reader, emphasizing viewpoints and processed information). This presence of the economics explains the rational-scientific features that most market data visualizations have compared to other journalistic visualizations. The users of market data are also rationally oriented. They look for data and pieces of news that are useful for them in their investments or work. Graphics are important to them, they tend to spend a lot of time with them, and the information that the graphics contain is more important for them than the aesthetics (Munk 1999). Our benchmarking showed that utility is more important in the field of online market data visualizations than aesthetics or attractiveness. There were no attractiveness-oriented executions, where illustrative elements would have been added for example to contextualize the data (i.e. an image of an oil barrel next tp the oil industry’s figures). Data is offered to readers in a – at least allegedly – neutral way. There are naturally practical reasons that support the choice of a plain or rational-scientific approach, avoiding illustrations, e.g. the fact that many topics are very abstract and difficult to illustrate. In brief, the rational-scientific approach (versus a more aesthetically-oriented or illustrative approach, described in section 2.3.1) to information visualization is the main, or even only approach, in market data visualizations.
6.2 Challenge to newsrooms
As mentioned earlier in the report, information visualization is by nature an extremely multidisciplinary field, and even more so is the production of interactive visualizations for digital platforms. In the context of journalistic work and newsrooms, where work division and processes have developed over centuries and are quite established (e.g. Hytönen 2013), the production of
11 Described in the forthcoming report, Next Media deliverable 1.2.3.4 Development of market data presentations
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infographics challenges the existing work division, skills and processes. Interactivity entered the field of information graphics around 1996 (Cairo 2013a, 185), and it needed to be learnt, invented and brought into the processes from scratch. Weber & Rall (2012, 1) see interactive infographics as an example of the hybridization that media convergence has produced: as one of the new media formats, genres and communication patterns that have emerged thanks to digital and technological development. Also production processes have hybridized. In the infographic production specific and new professional skills are needed. Giardina & Medina (2013) state that knowledge in graphic design, computer programming, statistics, illustration and traditional journalism should all be part of the infographic department employee’s skillset mix. Effective storytelling requires also skills familiar to movie directors (Gershon and Page 2001) and different multimedia skills, tools and a multimedia mind-set are required as the production of interactive graphics might include modelling virtual characters, animating them, film editing and adding interactive elements and incorporating sounds (Weber & Rall 2012, 23). In the production of market data visualizations statistical analysis, programming and data mining skills are of particular importance. Technical understanding is crucial regarding the interactivity of the visualizations. Our benchmarking study in the spring of 2013 showed that 56 per cent of the web sites we tested (which were big, well know services by big publishers) had interactive elements that could not be used on tablets (because they use Flash or Java). Only 44 per cent could be used with tablets – but they on the other hand might have problems on personal computers that use older versions of Internet Explorer. Also new workflow routines and practices are required. The main feature of the production process of infographics is teamwork with collaboration as a key factor, which requires extremely precise and clear communication between all parties involved, who might include engineers, designers, modellers, animators, sound designers, and so forth (Weber & Rall 2012, 24). An alternative for the teamwork of different professionals, as Weber and Rall (2012, 24) suggest, is the emergence of a new hybrid information designer, the “graphics editor.” This new professional field combines competences in journalism with mastery of visual arts and the respective software needed for production.
6.3 Shift from author-driven to reader-driven?
We have presented a fourfold table model for the classification of market data visualizations. While we believe that both journalistic and scientific approaches will prosper in the future (as long as those two genres exist), we expect that there will be a shift in market data visualizations from author-driver to reader-driven. Current trends would support this – big data and information visualization are gaining popularity and attention, and the market data available is of interest to
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many groups of people, from investors to business people and journalists. Also, the roles of producers and consumers have been converging and hybridizing, and the part of ”consumers” is all the more participatory and networked (i.e. Jenkins 2006; Jenkins & Deuze 2008).
Figure 150 Fourfold table of market data visualizations.
In the middle of the (ever-lasting) crisis of journalism we aspire that media companies would have the courage and wisdom to invest on the new narrative forms of communication, such as interactive infographics. Nowadays most information is available for free for anyone online. Publications cannot justify their existence just by compiling and presenting some of this data: instead they need to process it further – for example into illustrative visualizations and interactive information graphics where users can explore the data and make new discoveries by them selves. Interactive visualizations can also be seen as a marketing tool: they are easily shared and linked to in social media.
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