Visual Representation of Data Sets: A Document Design Foundation

The following piece was written for Writing 2209: Document Design course at Western University. It was submitted in partial fulfillment of the final project. You are more than welcome to use the information provided here by correctly citing the source; however, you may not use or submit this for an assignment.

The ability to represent statistical information in an efficient way, in both print and online, can be accomplished partially, if not entirely, by data visualization. In plain definition, data visualization is “the representation and presentation of data that exploits our visual perception abilities to amplify cognition” (Kirk 17).

It is a method to help the reader understand the importance of the data by transforming textual elements into forms of pictures and producing a visual image. Moreover, in Design Visual Language Charles Kostelnick and David Roberts say most readers prefer to look at pictures than to read data in text format; also the complexity of some data makes it extremely difficult for the user to understand and compare the textual data (245).

Different approaches can be taken into consideration when visually representing data; however, using the rhetorical situation to structuralize the thinking process and using cognate strategies, one can construct a cohesive and efficient data display and inform the reader about the subject. The purpose of this article is to develop an understanding of the visual representation of data and how to successfully use visual techniques for a better document design.

There are four key elements – representation, presentation, visual perception abilities and amplify cognition – in Kirk’s definition of data visualization.

  • REPRESENTATION

    The representation of data refers to the choice of physical forms (line, bar, or circle) to illustrate data.

  • PRESENTATION

    The presentation is concerned with the overall look of the document such as choice of color or annotations.

  • VISUAL PERCEPTION ABILITIES

    The visual perception abilities are related to the processing of information in the brain in an effective way using qualifications such as pattern matching and spatial reasoning.

  • AMPLIFY COGNITION

    The amplify cognition element is interested in maximizing these abilities and ultimately make the reader better informed about a subject.

The productivity of this definition clearly shows that data visualization has limited boundaries, and it is significantly expandable while providing an opportunity to change one’s view dramatically. However, these elements can be used as a guideline for document design development.

To be able to apply data visualization into document design process, it is convenient to follow several steps. As Kostelnick and Roberts mentions in their book, this process involves analyzing the rhetorical situation, invention, revision and visual editing (247).

First of all, to satisfy the rhetorical situation, the three essential elements – audience, purpose, and context – must be examined.

The audience, those who are capable of being influenced, can shift their understanding of the subject by how well the document is presenting its purpose in the given physical or psychological circumstances. The process involves finding the perfect interpretation of the intended audience; it answers the questions “who are they” and “what do they know”.

Not only it allows for a clear view of the purpose while identifying the expectations, but also influence the decisions based on the context that the data is going to be presented. For instance, one can distinguish the audience based on their interest in the subject. Perhaps a student interested in the field of psychology expects a more in-depth data representation of a particular topic than a person who simply reads for information.

Once the rhetorical situation is analyzed, and each situated rhetorical element is determined, one must develop the basic structure of data visualization. That is, to invent a possible method in which the data can be visually represented.

Invention

The invention step requires identifying the physical forms that lead to data representation; it is related to use of spatial elements. Because each data set, quantitative or qualitative, contains different types of data in order to achieve a different goal, one must choose a conventional form for constructing the data.

In an attempt to identify an appropriate visualization method for a specific communication purpose, Kirk explains that a bar chart can be used for comparison between categories, a pie chart can be used to clarify assessing hierarchies and part to whole relationships while a line chart can be used to show changed over time. Alternatively, one can use scatter plot to plot connections and relationships (Kirk 120).

In addition to above forms, geographic visualization of numerical data “utilizes sophisticated, interactive maps to explore information, guiding users through their data and providing context and information in with which to generate and explore hypothesis” (Maciejewski 49).

Such visualization is used during elections where the result in each region is expressed in a way to make it easier for the user to see the result. Lets look at an interactive map regarding the past Canadian federal election released by The Canadian Press.

The above interactive map allows the reader to select a province and see which party has won in that region. This method enhances the visualization of numerical results.

Selecting the proper visualization method can enhance the effectivity of the document; however, choosing a wrong convention can lead to massive technical error in which the outcome is not within the expectations.

Revision Process

The revision process allows for in-depth inspection of the chosen visualization method. It differentiates from the invention step regarding its interest in the integration of data representation into the final document.

The process shows that the primary measures taken to decide the proper visualization method can be revised as more information becomes available. For instance, the selection of a dot plot to compare categorical variables can be questioned once the same variables are plotted using a bar chart.

A quick observation of both plots determines that one particular case more accurately represents the objective. It also involves looking at the big picture, as a document in whole, to be consistent with other offered information.

Visual Editing

The final step, visual editing, is about applying cognate strategies to maximize how efficiently and effectively the document is presented and eventually result in informing the audience about the subject.

The six cognate strategies, as described by Kostelnick and Roberts, including arrangement, emphasis, clarity, conciseness, tone and ethos (13) can play a useful role in the enhancement of the visual representation of data sets.

  • ARRANGEMENT

    The arrangement offers the ability to organize the visual elements in a way that the reader can see their structure.

  • EMPHASIS

    Some parts of the visual method can be emphasized in order to highlight an important aspect of the data; different techniques, such as changing the color and bolding a line can be used as a form of emphasizing individual sections.

  • CLARITY

    For the user to understand the message, clarity becomes a critical principle; clarity in data display relies on visual annotations such as adding labels, captions and narrative (Kirk 113). In online documents, clarity can be achieved using interactivity (as shown on Page 2 using an interactive Canada map), since “dynamic, interactive visualizations can empower people to explore the data for themselves” (Murray 2).

  • CONCISENESS

    According to Kostelnick and Roberts, conciseness means “generating designs that are appropriately succinct within a particular situation” (19) which heavily relies on the rhetorical situation in terms of determining how much data is appropriate to be displayed in a given space.

  • TONE

    Visualized data is no different from a text concerning the tone (author’s voice in a given situation), and in data display tone collides with the invention step in order to determine the audience.

  • ETHOS

    The final strategy, ethos, focuses on how genuinely the data is being visualized; it is about telling the truth and not manipulating facts using graphical elements. Constructing false visualization of data results in misleading information and therefore question the credibility and quality of the document.

Data visualization has changed the way a document is presented to an audience.
Data visualizations “provide[s] an excellent approach for exploring data and are essential for presenting results” (Chen et al. 4), and if structured correctly then it can successfully accomplish their purpose: to inform the reader in an efficient and effective way about a subject.

How Might Climate Shift

Released by NASA’s Scientific Visualization Studio and NASA Center for Climate Simulation this video shows the magnitude of the shift in global temperatures that climate modelers predict, over the next century, if carbon dioxide concentrations continue their unabated climb. The temperature changes shown here are relative to the average temperatures observed from 1971-2000.

NASA’s Scientific Visualization Studio tries to highlight the importance of climate change using a 3D model of the Earth to show how the temperature changes throughout the years. Would this be effective if represented in a table? Perhaps not.
Data visualization focuses on the effectiveness of data while accuracy and truth-telling play a significant role.

Works Cited

  • Chen, Chun-houh, Wolfgang Hardle, and Antony Unwin. Handbook of Data Visualization. Berlin: Springer, 2007. Print.
  • Kirk, Andy. Data Visualization: a Successful Design Process. Birmingham, UK: Packt Publishing, 2012. Print.
  • Kostelnick, Charles, and David D. Roberts. Designing Visual Language: Strategies for Professional Communicators. Boston: Longman, 2011. Print.
  • Maciejewski, Ross. Data Representations, Transformations, and Statistics for Visual Reasoning. Morgan & Claypool Publishers, 2011. Print.
  • Murray, Scott. Interactive Data Visualization for the Web. Sebastopol, CA: O'Reilly Media, 2013. Print.