"A useful way to look at a data visualization challenge is to recognize that we are actually seeking to reduce choices. This is achieved through recognizing influential factors, by considering the desired function and tone of our work, familiarizing with our data and identifying stories. We are building clarity through selection and rejection. We are reducing the problem by enhancing our clarity." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"At its best, a static visualization is like a powerful photograph - a carefully conceived, arranged, and executed vision that manages to portray the sequence or motion of a story without the actual deployment of movement." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Data art is characterized by a lack of structured narrative and absence of any visual analysis capability. Instead, the motivation is much more about creating an artifact, an aesthetic representation or perhaps a technical/technique demonstration. At the extreme end, a design may be more guided by the idea of fun or playfulness or maybe the creation of ornamentation." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Data visualization is a means to an end, not an end in itself. It's merely a bridge connecting the messenger to the receiver and its limitations are framed by our own inherent irrationalities, prejudices, assumptions, and irrational tastes. All these factors can undermine the consistency and reliability of any predicted reaction to a given visualization, but that is something we can't realistically influence." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Done well, annotation can help explain and facilitate the viewing and interpretive experience. It is the challenge of creating a layer of user assistance and user insight: how can you maximize the clarity and value of engaging with this visualization design?" (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Histograms are often mistaken for bar charts but there are important differences. Histograms show distribution through the frequency of quantitative values (y axis) against defined intervals of quantitative values(x axis). By contrast, bar charts facilitate comparison of categorical values. One of the distinguishing features of a histogram is the lack of gaps between the bars [...]" (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Explanatory data visualization is about conveying information to a reader in a way that is based around a specific and focused narrative. It requires a designer-driven, editorial approach to synthesize the requirements of your target audience with the key insights and most important analytical dimensions you are wishing to convey." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Sparklines aren't necessarily a variation on the line chart, rather, a clever use of them. [...] They take advantage of our visual perception capabilities to discriminate changes even at such a low resolution in terms of size. They facilitate opportunities to construct particularly dense visual displays of data in small space and so are particularly applicable for use on dashboards." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"The art side of the field [data visualization] refers to the scope for unleashing design flair and encouraging innovation, where you strive to design communications that appeal on an aesthetic level and then survive in the mind on an emotional one." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"The best advice for guiding your decisions about using color is to refer to the two key rules [...] - make sure it is used unobtrusively and it does not mislead by implying representation when it shouldn't be. As with all design layers, the sensible objective here should be to strive for elegance rather than novelty, eye-candy, or attractiveness. To achieve this, it is important to be aware of the different functions, choices, and potential issues surrounding color deployment." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"The process of visual analysis can potentially go on endlessly, with seemingly infinite combinations of variables to explore, especially with the rich opportunities bigger data sets give us. However, by deploying a disciplined and sensible balance between deductive and inductive enquiry you should be able to efficiently and effectively navigate towards the source of the most compelling stories." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"[...] there is never a single path towards a 'best' solution. The inherent creativity and individualism of design work ensures that. An idealistic desire for a single and simple set of rules to achieve a guaranteed effective solution is simply unreasonable [...] There is, however, an established body of theoretical and practical evidence that guides us to understand which techniques work better for certain situations and less well for others. Importantly, these guides transcend instinct or personal taste and help us frame many of our design options, influencing the choices we make." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Visual metaphors are about integrating a certain visual quality in your work that somehow conveys that extra bit of connection between the data, the design, and the topic. It goes beyond just the choice of visual variable, though this will have a strong influence. Deploying the best visual metaphor is something that really requires a strong design instinct and a certain amount of experience." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"Visualization ethics relates to the potential deception that can be created, intentionally or otherwise, from an ineffective and inappropriate representation of data. Sometimes it can be through a simple lack of understanding of visual perception." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"With further similarities to small multiples, heatmaps enable us to perform rapid pattern matching to detect the order and hierarchy of different quantitative values across a matrix of categorical combinations. The use of a color scheme with decreasing saturation or increasing lightness helps create the sense of data magnitude ranking." (Andy Kirk, "Data Visualization: A successful design process", 2012)
"[Dashboards] are popular methods for displaying multiple visualizations and statistical information. Dashboards often take the form of some organizational instrument that offers both at-a-glance and detailed views of many different analytical and information dimensions. Dashboards are not a unique chart type themselves, but rather should be considered compositions that comprise multiple chart types." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"(1) Good data visualization is trustworthy: Is it reliable? Is the portrayal of the data and the subject faithful? Do the representation and presentation design have integrity? (2) Good data visualization is accessible: Is it usable? Is the portrayal of the data and the subject relevant? Is the representation and presentation design suitably understandable? (3) Good data visualization is elegant: Is it aesthetic? Is the representation and presentation design appealing?" (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"If the goal of data visualization […] is to facilitate understanding, all judgements made through the design process have to contribute to accomplishing this." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"Information design is a design practice concerned with the presentation of information. It is often associated with the activities of data visualization; indeed sometimes it is presented as the major field in which data visualization belongs. Unquestionably, both share an underlying motive to facilitate understanding. However, in my view, information design has a much broader application concerned with the design of many different forms of visual communication, particularly those with an instructional or functional slant, such as way-finding devices like hospital building maps or in the design of utility bills." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"The central premise in this book is that decision making is the key competency in data visualization: namely, effective decisions, efficiently made. To accomplish this you need to follow a design process that organizes your thinking and is underpinned by robust principles to optimize your thinking." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"The experience offered by a visualization influences the interpreting phase of understanding. Whereas tone embodies a continuum, the judgement of the most suitable experience is more distinct and concerns different methods of enabling interpretation: explanatory, exhibitory or exploratory. […] Explanatory visualizations offer an experience characterized by the visualizer taking responsibility to present important observations and interpretations to help the viewer more quickly assimilate the meaning of what is presented. […] Exploratory visualizations differ from explanatory in that they are focused more on helping the viewers or – more specifically in this case – the users discover and form their own interpretations. Almost universally, these types of works will be digital and interactive in nature. […] Exhibitory visualizations are characterized by being neither explicitly explanatory nor functionally exploratory. With exhibitory visualizations the viewers have to do the work to interpret meaning, relying on their own capacity to perceive and translate the features of a visualization." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"The term process contrasts considerably with procedure. The process […] provides a framework for thinking, rather than instructions to learn and follow. A good process should offer adaptability and remove the inflexibility of a defined procedure. In any visualization project, you will need to respond to revised requirements, additional data that emerges, or a shift in creative direction. A good process safeguards adaptability and cushions the impact of changing circumstances like these." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"[…] the term visual representation is arguably the quintessential activity of data visualization. Representation involves making decisions about how you are going to portray your data visually so that the subject understanding it offers can be made accessible to your audience. In simple terms, this is all about charts and the act of selecting the right chart to show the features of your data that you think are most relevant." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"There is an important distinction to make about the relationship between trust and truth. Achieving trust is an aim, presenting truth is an obligation. There should be no compromise here. You should never create work you know to be misleading, through either its content or its representation. You should never claim something presents the truth if it cannot be reasonably supported. The difference between a truth and an untruth should be beyond dispute. The fact that it is not, these days, is a sad indictment of modern society. Nevertheless, the imperative for truthfulness must be clear." (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
"When consuming a visualization, a viewer will go through a process of understanding involving three phases: perceiving, interpreting and comprehending. […] The first phase is perceiving, and this concerns the act of reading a chart: ‘what do I see?’. […] Interpreting […] translates these observations into quantitative and/or qualitative meaning. Interpreting involves assimilating what you have observed against what you know about the subject. What does what you have seen mean, given the subject? […] comprehending […] is the consequence or reflective legacy of the communication experience. The viewers now consider what the interpretations mean to themselves. What can be inferred as being important to you about the interpretations you have made?" (Andy Kirk, "Data Visualisation: A Handbook for Data Driven Design" 2nd Ed., 2019)
No comments:
Post a Comment