Fluent Essays

Please go through a (Chapter 1:Defining Data Visualization, Chapter 2:Visualization, Workflow, Chapter 3:Formulating Your Brief) in the attached text book and create a reflection. Each Chapter Reading Reflection should address the following prompts: Summarize the content of the chapter addressed. What were some of the highlights in this chapter and learning opportunities?Share some new ideas and/or thoughts that you developed from the reading of the chapter.How do you think you can apply this chapter’s concepts into your home, school, personal-life or work environment Reflection paper should be:two full pages for each chapter, so total 6 pages in totaldouble-spacedAPA formatting using citations when appropriate chap_1__2__3.docx Unformatted Attachment Preview 1 Defining Data Visualisation This opening chapter will introduce you to the subject of data visualisation, defining what data visualisation is and is not. It will outline the different ingredients that make it such an interesting recipe and establish a foundation of understanding that will form a key reference for all of the decision making you are faced with. Three core principles of good visualisation design will be presented that offer guiding ideals to help mould your convictions about distinguishing between effective and ineffective in data visualisation. You will also see how data visualisation sits alongside or overlaps with other related disciplines, and some definitions about the use of language in this book will be established to ensure consistency in meaning across all chapters. 1.1 The Components of Understanding To set the scene for what is about to follow, I think it is important to start this book with a proposed definition for data visualisation (Figure 1.1). This definition offers a critical term of reference because its components and their meaning will touch on every element of content that follows in this book. Furthermore, as a subject that has many different proposed definitions, I believe it is worth clarifying my own view before going further: F igure 1.1 A Definition for Data Visualisation At first glance this might appear to be a surprisingly short definition: isn’t there more to data visualisation than that, you might ask? Can nine words sufficiently articulate what has already been introduced as an eminently complex and diverse discipline? I have arrived at this after many years of iterations attempting to improve the elegance of my definition. In the past I have tried to force too many words and too many clauses into one statement, making it cumbersome and rather undermining its value. Over time, as I have developed greater clarity in my own convictions, I have in turn managed to establish greater clarity about what I feel is the real essence of this subject. The definition above is, I believe, a succinct and practically useful description of what the pursuit of visualisation is truly about. It is a definition that largely informs the contents of this book. Each chapter will aim to enlighten you about different aspects of the roles of and relationships between each component expressed. Let me introduce and briefly examine each of these one by one, explaining where and how they will be discussed in the book. Firstly, data, our critical raw material. It might appear a formality to mention data in the definition for, after all, we are talking about data visualisation as opposed to, let’s say, cheese visualisation (though visualisation of data using cheese has happened, see Figure 1.2), but it needs to be made clear the core role that data has in the design process. Without data there is no visualisation; indeed there is no need for one. Data plays the fundamental role in this work, so you will need to give it your undivided attention and respect. You will discover in Chapter 4 the importance of developing an intimacy with your data to acquaint yourself with its physical properties, its meaning and its potential qualities. F igure 1.2 Per Capita Cheese Consumption in the US Data is names, amounts, groups, statistical values, dates, comments, locations. Data is textual and numeric in format, typically held in datasets in table form, with rows of records and columns of different variables. This tabular form of data is what we will be considering as the raw form of data. Through tables, we can look at the values contained to precisely read them as individual data points. We can look up values quite efficiently, scanning across many variables for the different records held. However, we cannot easily establish the comparative size and relationship between multiple data points. Our eyes and mind are not equipped to translate easily the textual and numeric values into quantitative and qualitative meaning. We can look at the data but we cannot really see it without the context of relationships that help us compare and contrast them effectively with other values. To derive understanding from data we need to see it represented in a different, visual form. This is the act of data repres entatio n. This word representation is deliberately positioned near the front of the definition because it is the quintessential activity of data visualisation design. Representation concerns the choices made about the form in which your data will be visually portrayed: in lay terms, what chart or charts you will use to exploit the brain’s visual perception capabilities most effectively. When data visualisers create a visualisation they are representing the data they wish to show visually through combinations of marks and attributes. Marks are points, lines and areas. Attributes are the appearance properties of these marks, such as the size, colour and position. The recipe of these marks and their attributes, along with other components of apparatus, such as axes and gridlines, form the anatomy of a chart. In Chapter 6 you will gain a deeper and more sophisticated appreciation of the range of different charts that are in common usage today, broadening your visual vocabulary. These charts will vary in complexity and composition, with each capable of accommodating different types of data and portraying different angles of analysis. You will learn about the key ingredients that shape your data representation decisions, explaining the factors that distinguish the effective from the ineffective choices. Beyond representation choices, the pres entatio n of data concerns all the other visible design decisions that make up the overall visualisation anatomy. This includes choices about the possible applications of interactivity, features of annotation, colour usage and the composition of your work. During the early stages of learning this subject it is sensible to partition your thinking about these matters, treating them as isolated design layers. This will aid your initial critical thinking. Chapters 7–10 will explore each of these layers in depth, profiling the options available and the factors that influence your decisions. However, as you gain in experience, the interrelated nature of visualisation will become much more apparent and you will see how the overall design anatomy is entirely connected. For instance, the selection of a chart type intrinsically leads to decisions about the space and place it will occupy; an interactive control may be included to reveal an annotated caption; for any design property to be even visible to the eye it must possess a colour that is different from that of its background. The goal expressed in this definition states that data visualisation is about facilitating unders tanding. This is very important and some extra time is required to emphasise why it is such an influential component in our thinking. You might think you know what understanding means, but when you peel back the surface you realise there are many subtleties that need to be acknowledged about this term and their impact on your data visualisation choices. Understanding ‘understanding’ (still with me?) in the context of data visualisation is of elementary significance. When consuming a visualisation, the viewer will go through a process of understanding involving three stages: perceiving, interpreting and comprehending (Figure 1.3). Each stage is dependent on the previous one and in your role as a data visualiser you will have influence but not full control over these. You are largely at the mercy of the viewer – what they know and do not know, what they are interested in knowing and what might be meaningful to them – and this introduces many variables outside of your control: where your control diminishes the influence and reliance on the viewer increases. Achieving an outcome of understanding is therefore a collective responsibility between visualiser and viewer. These are not just synonyms for the same word, rather they carry important distinctions that need appreciating. As you will see throughout this book, the subtleties and semantics of language in data visualisation will be a recurring concern. F igure 1.3 The Three Stages of Understanding Let’s look at the characteristics of the different stages that form the process of understanding to help explain their respective differences and mutual dependencies. Firstly, perceiving. This concerns the act of simply being able to read a chart. What is the chart showing you? How easily can you get a sense of the values of the data being portrayed? Where are the largest, middle-sized and smallest values? What proportion of the total does that value hold? How do these values compare in ranking terms? To which other values does this have a connected relationship? The notion of understanding here concerns our attempts as viewers to efficiently decode the representations of the data (the shapes, the sizes and the colours) as displayed through a chart, and then convert them into perceived values: estimates of quantities and their relationships to other values. Interpreting is the next stage of understanding following on from perceiving. Having read the charts the viewer now seeks to convert these perceived values into some form of meaning: Is it good to be big or better to be small? What does it mean to go up or go down? Is that relationship meaningful or insignificant? Is the decline of that category especially surprising? The viewer’s ability to form such interpretations is influenced by their pre-existing knowledge about the portrayed subject and their capacity to utilise that knowledge to frame the implications of what has been read. Where a viewer does not possess that knowledge it may be that the visualiser has to address this deficit. They will need to make suitable design choices that help to make clear what meaning can or should be drawn from the display of data. Captions, headlines, colours and other annotated devices, in particular, can all be used to achieve this. Comprehending involves reasoning the consequence of the perceiving and interpreting stages to arrive at a personal reflection of what all this means to them, the viewer. How does this information make a difference to what was known about the subject previously? Why is this relevant? What wants or needs does it serve? Has it confirmed what I knew or possibly suspected beforehand or enlightened me with new knowledge? Has this experience impacted me in an emotional way or left me feeling somewhat indifferent as a consequence? Does the context of what understanding I have acquired lead me to take action – such as make a decision or fundamentally change my behaviour – or do I simply have an extra grain of knowledge the consequence of which may not materialise until much later? Over the page is a simple demonstration to further illustrate this process of understanding. In this example I play the role of a viewer working with a sample isolated chart (Figure 1.4). As you will learn throughout the design chapters, a chart would not normally just exist floating in isolation like this one does, but it will serve a purpose for this demonstration. Figure 1.4 shows a clustered bar chart that presents a breakdown of the career statistics for the footballer Lionel Messi during his career with FC Barcelona. The process commences with perceiving the chart. I begin by establishing what chart type is being used. I am familiar with this clustered bar chart approach and so I quickly feel at ease with the prospect of reading its display: there is no learning for me to have to go through on this occasion, which is not always the case as we will see. I can quickly assimilate what the axes are showing by examining the labels along the x- and y-axes and by taking the assistance provided by colour legend at the top. I move on to scanning, detecting and observing the general physical properties of the data being represented. The eyes and brain are working in harmony, conducting this activity quite instinctively without awareness or delay, noting the most prominent features of variation in the attributes of size, shape, colour and position. F igure 1.4 Demonstrating the Process of Understanding I look across the entire chart, identifying the big, small and medium values (these are known as stepped magnitude judgements), and form an overall sense of the general value rankings (global comparison judgements). I am instinctively drawn to the dominant bars towards the middle/right of the chart, especially as I know this side of the chart concerns the most recent career performances. I can determine that the purple bar – showing goals – has been rising pretty much year-on-year towards a peak in 2011/12 and then there is a dip before recovery in his most recent season. My visual system is now working hard to decode these properties into estimations of quantities (amounts of things) and relationships (how different things compare with each other). I focus on judging the absolute magnitudes of individual bars (one bar at a time). The assistance offered by the chart apparatus, such as the vertical axis (or y- axis) values and the inclusion of gridlines, is helping me more quickly estimate the quantities with greater assurance of accuracy, such as discovering that the highest number of goals scored was around 73. I then look to conduct some relative higher/lower comparisons. In comparing the games and goals pairings I can see that three out of the last four years have seen the purple bar higher than the blue bar, in contrast to all the rest. Finally I look to establish proportional relationships between neighbouring bars, i.e. by how much larger one is compared with the next. In 2006/07 I can see the blue bar is more than twice as tall as the purple one, whereas in 2011/12 the purple bar is about 15\% taller. By reading this chart I now have a good appreciation of the quantities displayed and some sense of the relationship between the two measures, games and goals. The second part of the understanding process is interpreting. In reality, it is not so consciously consecutive or delayed in relationship to the perceiving stage but you cannot get here without having already done the perceiving. Interpreting, as you will recall, is about converting perceived ‘reading’ into meaning. Interpreting is essentially about orientating your assessment of what you’ve read against what you know about the subject. As I mentioned earlier, often a data visualiser will choose to – or have the opportunity to – share such insights via captions, chart overlays or summary headlines. As you will learn in Chapter 3, the visualisations that present this type of interpretation assistance are commonly described as offering an ‘explanatory’ experience. In this particular demonstration it is an example of an ‘exhibitory’ experience, characterised by the absence of any explanatory features. It relies on the viewer to handle the demands of interpretation without any assistance. As you will read about later, many factors influence how well different viewers will be able to interpret a visualisation. Some of the most critical include the level of interest shown towards the subject matter, its relevance and the general inclination, in that moment, of a viewer to want to read about that subject through a visualisation. It is also influenced by the knowledge held about a subject or the capacity to derive meaning from a subject even if a knowledge gap exists. Returning to the sample chart, in order to translate the quantities and relationships I extracted from the perceiving stage into meaning, I am effectively converting the reading of value sizes into notions of good or bad and comparative relationships into worse than or better than etc. To interpret the meaning of this data about Lionel Messi I can tap into my passion for and knowledge of football. I know that for a player to score over 25 goals in a season is very good. To score over 35 is exceptional. To score over 70 goals is frankly preposterous, especially at the highest level of the game (you might find plenty of players achieving these statistics playing for the Dog and Duck pub team, but these numbers have been achieved for Barcelona in La Liga, the Champions League and other domestic cup competitions). I know from watching the sport, and poring over statistics like this for 30 years, that it is very rare for a player to score remotely close to a ratio of one goal per game played. Those purple bars that exceed the height of the blue bars are therefore remarkable. Beyond the information presented in the chart I bring knowledge about the periods when different managers were in charge of Barcelona, how they played the game, and how some organised their teams entirely around Messi’s talents. I know which other players were teammates across different seasons and who might have assisted or hindered his achievements. I also know his age and can mentally compare his achievements with the traditional football career arcs that will normally show a steady rise, peak, plateau, and then decline. Therefore, in this example, I am not just interested in the subject but can bring a lot of knowledge to aid me in interpreting this analysis. That helps me understand a lot more about what this data means. For other people they might be passingly interested in football and know how to read what is being presented, but they might not possess the domain knowledge to go deeper into the interpretation. They also just might not care. Now imagine this was analysis of, let’s say, an NHL ice hockey player (Figure 1.5) – that would present an entirely different challenge for me. In this chart the numbers are irrelevant, just using the same chart as before with different labels. Assuming this was real analysis, as a sports fan in general I would have the capacity to understand the notion of a sportsperson’s career statistics in terms of games played and goals scored: I can read the chart (perceiving) that shows me this data and catch the gist of the angle of analysis it is portraying. However, I do not have sufficient domain knowledge of ice hockey to determine the real meaning and significance of the big–small, higher– lower value relationships. I cannot confidently convert ‘small’ into ‘unusual’ or ‘greater than’ into ‘remarkable’. My capacity to interpret is therefore limited, and besides I have no connection to the subject matter, so I am insufficiently interested to put in the effort to spend much time with any in-depth attempts at interpretation. F igure 1.5 Demonstrating the Process of Understanding Imagine this is now no longer analysis about sport but about the sightings in the wild of Winglets and Spungles (completely made up words). Once again I can still read the chart shown in Figure 1.6 but now I have absolutely no connection to the subject whatsoever. No knowledge and no interest. I have no idea what these things are, no understanding about the sense of scale that should be expected for these sightings, I don’t know what is good or bad. And I genuinely don’t care either. In contrast, for those who do have a knowledge of and interest in the subject, the meaning of this data will be much more relevant. They will be able to read the chart and make some sense of the meaning of the quantities and relationships displayed. To help with perceiving, viewers need the context of scale. To help with interpreting, viewers need the context of subject, whether that is provided by the visualiser or the viewer themself. The challenge for you and I as data visualisers is to determine what our audience will know already and what they will need to know in order to possibly assist them in interpreting the meaning. The use of explanatory captions, perhaps positioned in tha ... Purchase answer to see full attachment