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Visualizations That Really Work

• Scott Berinato

Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone—but that convenience can lead to charts that are merely adequate or even ineffective.

By answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? and Am I declaring something or exploring something? He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz.

This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.

Know what message you’re trying to communicate before you get down in the weeds.

Idea in Brief

Knowledge workers need greater visual literacy than they used to, because so much data—and so many ideas—are now presented graphically. But few of us have been taught data-visualization skills.

Tools Are Fine…

Inexpensive tools allow anyone to perform simple tasks such as importing spreadsheet data into a bar chart. But that means it’s easy to create terrible charts. Visualization can be so much more: It’s an agile, powerful way to explore ideas and communicate information.

…But Strategy Is Key

Don’t jump straight to execution. Instead, first think about what you’re representing—ideas or data? Then consider your purpose: Do you want to inform, persuade, or explore? The answers will suggest what tools and resources you need.

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.

• Scott Berinato is a senior editor at Harvard Business Review and the author of Good Charts Workbook: Tips Tools, and Exercises for Making Better Data Visualizations and Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations .

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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

Access your free e-book today.

What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

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Painting Pictures with Data: The Power of Visual Representations

Picture this. A chaotic world of abstract concepts and complex data, like a thousand-piece jigsaw puzzle. Each piece, a different variable, a unique detail.

Alone, they’re baffling, nearly indecipherable.

But together? They’re a masterpiece of visual information, a detailed illustration.

American data pioneer Edward Tufte , a notable figure in the graphics press, believed that the art of seeing is not limited to the physical objects around us. He stated, “The commonality between science and art is in trying to see profoundly – to develop strategies of seeing and showing.”

It’s in this context that we delve into the world of data visualization. This is a process where you create visual representations that foster understanding and enhance decision making.

It’s the transformation of data into visual formats. The information could be anything from theoretical frameworks and research findings to word problems. Or anything in-between. And it has the power to change the way you learn, work, and more.

And with the help of modern technology, you can take advantage of data visualization easier than ever today.

What are Visual Representations?

Think of visuals, a smorgasbord of graphical representation, images, pictures, and drawings. Now blend these with ideas, abstract concepts, and data.

You get visual representations . A powerful, potent blend of communication and learning.

As a more formal definition, visual representation is the use of images to represent different types of data and ideas.

They’re more than simply a picture. Visual representations organize information visually , creating a deeper understanding and fostering conceptual understanding. These can be concrete objects or abstract symbols or forms, each telling a unique story. And they can be used to improve understanding everywhere, from a job site to an online article. University professors can even use them to improve their teaching.

But this only scratches the surface of what can be created via visual representation.

Types of Visual Representation for Improving Conceptual Understanding

Graphs, spider diagrams, cluster diagrams – the list is endless!

Each type of visual representation has its specific uses. A mind map template can help you create a detailed illustration of your thought process. It illustrates your ideas or data in an engaging way and reveals how they connect.

Here are a handful of different types of data visualization tools that you can begin using right now.

1. Spider Diagrams

Spider diagrams , or mind maps, are the master web-weavers of visual representation.

They originate from a central concept and extend outwards like a spider’s web. Different ideas or concepts branch out from the center area, providing a holistic view of the topic.

This form of representation is brilliant for showcasing relationships between concepts, fostering a deeper understanding of the subject at hand.

2. Cluster Diagrams

As champions of grouping and classifying information, cluster diagrams are your go-to tools for usability testing or decision making. They help you group similar ideas together, making it easier to digest and understand information.

They’re great for exploring product features, brainstorming solutions, or sorting out ideas.

3. Pie Charts

Pie charts are the quintessential representatives of quantitative information.

They are a type of visual diagrams that transform complex data and word problems into simple symbols. Each slice of the pie is a story, a visual display of the part-to-whole relationship.

Whether you’re presenting survey results, market share data, or budget allocation, a pie chart offers a straightforward, easily digestible visual representation.

4. Bar Charts

If you’re dealing with comparative data or need a visual for data analysis, bar charts or graphs come to the rescue.

Bar graphs represent different variables or categories against a quantity, making them perfect for representing quantitative information. The vertical or horizontal bars bring the data to life, translating numbers into visual elements that provide context and insights at a glance.

Visual Representations Benefits

1. deeper understanding via visual perception.

Visual representations aren’t just a feast for the eyes; they’re food for thought. They offer a quick way to dig down into more detail when examining an issue.

They mold abstract concepts into concrete objects, breathing life into the raw, quantitative information. As you glimpse into the world of data through these visualization techniques , your perception deepens.

You no longer just see the data; you comprehend it, you understand its story. Complex data sheds its mystifying cloak, revealing itself in a visual format that your mind grasps instantly. It’s like going from a two dimensional to a three dimensional picture of the world.

2. Enhanced Decision Making

Navigating through different variables and relationships can feel like walking through a labyrinth. But visualize these with a spider diagram or cluster diagram, and the path becomes clear. Visual representation is one of the most efficient decision making techniques .

Visual representations illuminate the links and connections, presenting a fuller picture. It’s like having a compass in your decision-making journey, guiding you toward the correct answer.

3. Professional Development

Whether you’re presenting research findings, sharing theoretical frameworks, or revealing historical examples, visual representations are your ace. They equip you with a new language, empowering you to convey your message compellingly.

From the conference room to the university lecture hall, they enhance your communication and teaching skills, propelling your professional development. Try to create a research mind map and compare it to a plain text document full of research documentation and see the difference.

4. Bridging the Gap in Data Analysis

What is data visualization if not the mediator between data analysis and understanding? It’s more than an actual process; it’s a bridge.

It takes you from the shores of raw, complex data to the lands of comprehension and insights. With visualization techniques, such as the use of simple symbols or detailed illustrations, you can navigate through this bridge effortlessly.

5. Enriching Learning Environments

Imagine a teaching setting where concepts are not just told but shown. Where students don’t just listen to word problems but see them represented in charts and graphs. This is what visual representations bring to learning environments.

They transform traditional methods into interactive learning experiences, enabling students to grasp complex ideas and understand relationships more clearly. The result? An enriched learning experience that fosters conceptual understanding.

6. Making Abstract Concepts Understandable

In a world brimming with abstract concepts, visual representations are our saving grace. They serve as translators, decoding these concepts into a language we can understand.

Let’s say you’re trying to grasp a theoretical framework. Reading about it might leave you puzzled. But see it laid out in a spider diagram or a concept map, and the fog lifts. With its different variables clearly represented, the concept becomes tangible.

Visual representations simplify the complex, convert the abstract into concrete, making the inscrutable suddenly crystal clear. It’s the power of transforming word problems into visual displays, a method that doesn’t just provide the correct answer. It also offers a deeper understanding.

How to Make a Cluster Diagram?

Ready to get creative? Let’s make a cluster diagram.

First, choose your central idea or problem. This goes in the center area of your diagram. Next, think about related topics or subtopics. Draw lines from the central idea to these topics. Each line represents a relationship.

While you can create a picture like this by drawing, there’s a better way.

Mindomo is a mind mapping tool that will enable you to create visuals that represent data quickly and easily. It provides a wide range of templates to kick-start your diagramming process. And since it’s an online site, you can access it from anywhere.

With a mind map template, creating a cluster diagram becomes an effortless process. This is especially the case since you can edit its style, colors, and more to your heart’s content. And when you’re done, sharing is as simple as clicking a button.

A Few Final Words About Information Visualization

To wrap it up, visual representations are not just about presenting data or information. They are about creating a shared understanding, facilitating learning, and promoting effective communication. Whether it’s about defining a complex process or representing an abstract concept, visual representations have it all covered. And with tools like Mindomo , creating these visuals is as easy as pie.

In the end, visual representation isn’t just about viewing data, it’s about seeing, understanding, and interacting with it. It’s about immersing yourself in the world of abstract concepts, transforming them into tangible visual elements. It’s about seeing relationships between ideas in full color. It’s a whole new language that opens doors to a world of possibilities.

The correct answer to ‘what is data visualization?’ is simple. It’s the future of learning, teaching, and decision-making.

Keep it smart, simple, and creative! The Mindomo Team

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Representation in Art – Definition, Examples, History & More – Art Theory Glossary

Table of Contents

I. What is Representation in Art?

Representation in art refers to the portrayal or depiction of subjects, objects, or ideas in a visual form. It is the artist’s interpretation of reality, whether it be realistic, abstract, or somewhere in between.

Representation can take many forms, including paintings, sculptures, drawings, photographs, and more. It is a way for artists to communicate their thoughts, emotions, and perspectives to the viewer.

II. Historical Perspectives on Representation in Art

Throughout history, representation in art has evolved and changed in response to cultural, societal, and technological advancements. In ancient civilizations, art was often used to depict religious or mythological stories, with a focus on realism and detail.

During the Renaissance period, artists such as Leonardo da Vinci and Michelangelo revolutionized representation in art by incorporating perspective, light, and shadow to create more lifelike images. This period also saw the rise of portraiture as a popular form of representation.

In the 19th and 20th centuries, movements such as Impressionism, Cubism, and Surrealism challenged traditional notions of representation in art, pushing boundaries and exploring new ways of depicting the world.

III. Cultural and Societal Influences on Representation in Art

Cultural and societal influences play a significant role in shaping representation in art. Different cultures have unique artistic traditions and styles that reflect their values, beliefs, and histories.

For example, traditional African art often focuses on symbolism and abstraction, while Western art tends to prioritize realism and perspective. These cultural differences impact how artists choose to represent their subjects and ideas.

Societal issues such as politics, gender, race, and class also influence representation in art. Artists may use their work to challenge stereotypes, advocate for social change, or highlight marginalized voices within society.

IV. The Role of the Artist in Representation

The artist plays a crucial role in determining how subjects are represented in art. They make decisions about composition, color, style, and technique that shape the viewer’s interpretation of the artwork.

Artists have the power to challenge conventions, provoke thought, and evoke emotion through their representations. They can choose to depict reality as it is, or they can distort, abstract, or reimagine it in new and unexpected ways.

V. Contemporary Approaches to Representation in Art

In the contemporary art world, representation has become increasingly diverse and experimental. Artists are exploring new mediums, techniques, and concepts to push the boundaries of traditional representation.

Some artists are using technology, such as virtual reality and digital media, to create immersive and interactive representations. Others are incorporating performance, installation, and multimedia elements into their work to challenge traditional notions of representation.

VI. The Impact of Representation in Art on Society

Representation in art has a profound impact on society, shaping how we perceive ourselves, others, and the world around us. Art can challenge stereotypes, inspire empathy, and provoke critical thinking about social issues.

By representing diverse perspectives, experiences, and identities, artists can promote inclusivity, diversity, and social justice. Art has the power to spark conversations, foster understanding, and create positive change within society.

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Visual representation.

The study and conceptualization of visual representation were primarily associated with art and art history prior to the twentieth century, and drew on the analytical tools of iconology with a focus on the artist’s intention and perception. With the advent of  semiotics, followed by other theories of the visual, the twentieth century marked a broadening in conceptions of visual representation from the realm of art to the realm of the everyday. Studies of visual representation have expanded to include the images that surround people every-day. This includes studies of images in film (Metz 1990), the use of photography (Sontag 1979), advertising (Goffman 1979), scientific imagery (Latour & Woolgar 1986), learning and development (Kress 1996), and the representation of social identities (Hall 1997). This expansion of the domain of the visual has influenced how visual representation is theorized and approached, including a shift in focus from the image to contexts of production and viewers. Today a range of theories is applied to understanding the visual, including theories drawn from anthropology, art history, cognitive psychology, cultural studies, linguistics, psychoanalytical theories, and sociology.

Studies of visual representation have shown that the visual is central to the cultural construction of social life. People’s everyday experience of the world, socially, physically, and psychologically, and their production of meaning are mediated by the visual. Visual representation is not only a matter of how people experience the world; it is also crucial in how the world itself is constructed. The twenty-first century is marked by a plethora of imaging and visual technologies, and in contemporary western society everyday life is saturated with the images that these technologies make available. Studies of late twentieth century culture have noted a turn to the visual (Mitchell 1995; Mirzoeff 1999) in which the modern world has become a visual phenomenon; a world that conflates looking, seeing, and knowing (Jenks 1995) to become a “vision machine” created through new visualizing technologies in which people are all caught (Virilio 1994).

Visual representation is a complex concept that connects with fundamental questions of “reality,” ideology and power, agency, as well as signification and the procedures and potentials for interpreting meaning. There is, however, a general agreement that the meaning of an image is “made” at three sites: (1) the site of the image, (2) the site of production, and (3) the viewer interaction. The meaning of visual representation is realized in the interplay across these three sites or factors.

The concept of visual codes and conventions (socially agreed ways of doing something) is often employed in the analysis of visual representations. Semiotics proposes that there is a wide range of visual, pictorial, material, and symbolic signs that are conventional in the way that they simplify, and yet bear some kind of resemblance to, an object or quality in the “real” world that they signify. This includes abstract signs that become associated through agreement into convention with the object or quality represented. These conventions are extended to a semiotic “code” to form an extended system of semiotic signs. Social semiotics uses the concept of “semiotic resource” rather than code. This serves to foreground the work of the sign-maker in interpreting and assembling semiotic resources.

What is depicted in an image and how it is represented are an obvious starting point for understanding the process of visual representation. Visual codes can, for example, be designed to realize visual narratives or conceptual categories, each of which serves to connect or disconnect depicted elements in different ways. That is, the way these relationships are represented marks (and establishes) what belongs and does not belong together, who acts on what, and so on. These represented relations are realized through a range of codes, including the representation of depicted elements, compositional arrangements, and analytical structures. Visual representations also work by using and representing many of the visual codes that are employed in lived rather than textual forms of communication. In this way visual representations can carry all the sign systems and codes (dress, style, body language, and so on) that can “make” the lived world meaningful. For instance, Goffman’s Gender advertisements (1979) offers a classic exemplar of how depicted elements, compositional structures, posture, and gaze contribute to the production and regulation of gendered identifies.

In addition to their “content,” visual representations position the viewer to look in particular ways. In this way an image can be understood as “telling” viewers who they are and where they are. In doing so the image realizes the ideological design of the viewer’s relationship with the depicted object, person, or event. The viewer can refuse to adopt the viewing position offered by an image, but nonetheless it is present.

In order to get at the meaning of an image, the ways in which the meanings and uses of images are regulated by institutions of production, distribution, and consumption need to be considered. The economics, motives, and intentions of those who produce and disseminate visual representations are aspects of the site of production. That is, visual representations need to be understood in context because these social factors and experiences are not separate from the signifying systems of the visual; they do not exist in an abstract realm – they actually structure it.

The production of an image is therefore integral to its use and meaning. Technologies of production, in particular new technologies, are key to the question of the relationship between visual representation and “reality.” Technologies have changed what it is possible to see and how these entities are seen, through the magnification of detail, slow motion, capturing images that escape “natural” vision, and the changing context of viewing.

Viewer And Gaze

The viewer, looking, and subjectivity are key themes in theorizing and understanding visual representation. Understanding the agency of the viewer demands a shift of analytical emphasis away from the image or text to the social identities and experiences of the viewer. This necessarily connects with the context of viewing as part of the production of meaning. From this perspective, meaning is understood as constituted in the articulation between the viewer and the viewed, between the power of the image to signify and the viewer’s capacity to interpret meaning. Visual representations work (mean) by producing effects each time they are looked at. However, the image also depends for its effects on a certain way of seeing.

Asking who the viewer is raises the question of the alignment and identification of their points of view and perspective with those of the maker of the image. In other words, both the maker and the reader of a visual representation are involved in the process of making sense ( designing meaning ). Seeing the viewer as agentive means that the power of a visual sign remains a potential until the viewer engages with it. The parameters within which interpretations are made are shaped by the viewer’s social position and subjective capacity in conjunction with the conventions and codes of a visual representation.

The mediating effect of contexts of viewing is especially relevant for visual representation in the twenty-first century, when images are mobile across different media and contexts. For example, reading a magazine at home, driving by a billboard on the road, and watching an advert at home on television or with friends in the cinema all call forth different practices of viewing. The context of viewing contributes to realizing different practices of looking and interpreting.

In summary, visual representation can be understood as a cycle of production , circulation, and consumption, a cycle in which visual representations become a site of struggle over what something means.

Visual Representation And Signification

Signification and how to theorize the relationship between referent, signifier, and signified are central to the way visual representation is conceptualized. Current theories of perception, structuralism, poststructuralism, and postmodernism theorize their relationship in different ways. This influences how the power of visual representation is understood to shape people’s experience of the world, what the world is, and what it can be.

Models of perception are concerned with image-making as an internal psychological process, that is, with the perceptions and sensations in the mind of the image-maker and the viewer. Here the goal is the perfect reproduction of reality. This places reality as external to representation, in which the referent “exists” and the signifier reflects it.

From this perspective an image is a transparent window through which the viewer can connect directly (via the signifier) to an original object or presence (the referent). This presents a notion of the image as autonomous and as containing within itself an inherent meaning. This conception of visual signification and image-making as cognition, perception, and a kind of optical truth severs the connection between image and society and fails to account for the relationship between image and power.

Structuralism

In the twentieth century semiotics began to interrogate the elements and structures of systems of representation, language, and the visual. Traditional semiotics (e.g., Saussure 1986) views meaning as consisting of two parts: the signifier and the signified. On the one hand, the signifier denotes a sign’s literal meaning: that is, who or what is depicted. On the other hand, the signified connotes a sign’s associated meanings: that is, the cultural ideas and values associated with the objects depicted. Structuralism asserts that although we depend on the relationship between these two parts to produce meaning, it is an arbitrary relationship established through cultural convention. This presents language and other systems of meaning as self-contained structures, a system of arbitrary and differential signs that do not name or reflect thoughts or concepts that pre-exist it, but rather actively construct them.

Structuralism attempts to understand the social and cultural forces that motivate representation, rather than accepting representation as given or natural. This brings the relationship between referent, signifier, and signified into a social relationship. Viewing is repositioned as a social process that involves the unfolding of meaning through the “activation” of visual codes of recognition, codes learnt through the viewer’s interaction in the social cultural world, rather than an individual physiological and cognitive process.

Locating visual representation in the social domain in this way opens up the necessity of thinking of visual representation as discursive work, work that produces society. This reconnects images with the social and in doing so locates image in relation to power. However, structuralism has been criticized for being ahistorical and for being deterministic in the way that it privileges structure over individual human agency. In this way it is inadequate in theorizing the potential of human agency to resist, challenge, and change systems of representation. Peirce (1991) brought human agency into semiosis by his assertion that sign-making involves the cooperation of a sign, its object, and its interpretant. In other words, for Pierce signs cannot exist independently of a subject to interpret them. This shifts attention from the idea of meaning as embedded in text to a concern for the interactive process of meaning-making .

Poststructuralism

Poststructuralism developed in the 1960s as a critical response to the assumptions of structuralism. Poststructuralism rejects the structuralist notion that there is a consistent structure to a text and argues instead that these structures and systems that underlie a text are themselves a cultural product. Poststructuralism argues that the systems of knowledge that produce it, as well as the text itself, need to be analyzed in order to understand the meaning of a text. In other words, poststructuralism can be seen as the study of the production of knowledge (Worth 1981).

Poststructuralism argues that the author is not the primary source of the meaning of a text (Barthes 1977) and that there is a multiplicity of meanings to any text. Decentering the authority of the author in this way serves to open up the meaning of a text to the work of the reader and other sources for meaning, such as cultural norms and historical context. Placing the reader at the center of meaning-making in this way asserts that no text has one meaning, purpose, or existence. In addition to demanding a rethinking of texts and representations, poststructuralism also destabilizes the notion of the “self.” From this perspective the concept of the self as a single and coherent entity is a fictional construct.

Social semiotics rejects the concept of code as too rigid and static and instead uses the concept of semiotic resource (Hodge & Kress 1988). A focus on semiotic resources serves to recognize the social, historical, and cultural character of visual codes and the ideological work of signs. From a poststructuralist perspective the act of reading a sign is repositioned as the act of sign-making, of giving meaning to a signified. That is, the reader is the signmaker and signs are constantly remade and transformed through people’s engagement with them. In this way, the denotative (the signified) can be seen as always connotative, no matter how literal its relationship to the “real” world.

The relationship of the referent with the signifier and signified is significantly “loosened” in poststructuralism- and the term “signifier” is preferred to “sign” to indicate that no single meaning is secured to a word, and that the possibility of a full and secure meaning is always deferred (Derrida 1978). In other words, meaning is never as fully “fixed” as structuralism appears to suggest. This attempts to move away from the linking of representation with the idea of reconstituting the missing “presence,” the original source “content” of the empirical form of a representation.

Postmodernism

Postmodernism argues that the economic and social conditions of late capitalism, in particular globalization and new technologies, have led to a decentralized, fragmented, and media-dominated society saturated with images. Postmodernism rejects the idea of “representation” in favor of “ simulation ” to emphasize the constructed character of signs. Simulations are understood as simulacra of the “real” that presuppose and precede the “real.” These hyperreal symbols and signs do not have an original, stable referent or a source of meaning. In this way postmoderism breaks the relationship between seeing and knowing. Baudrillard (1983) argues that society has replaced all reality and meaning with symbols and signs, and that what people understand as “reality” is actually a simulation of reality. From this perspective, images are to be fundamentally mistrusted because they claim to signal a referent that they have become detached or severed from.

Postmodernism is a contested term. Whether or not it is a distinct historical period or an extension of modernism remains a matter of debate. There is agreement, however, that the conditions of late capitalism have realized some of the aspects encompassed in postmodernist theory, such as the breakdown of traditional structures (e.g., genre and other stylistic forms) and classificatory categories. This conceptualization of visual representation refutes the possibility of representation as a mimetic reflection of “reality.”

Visual Representation, Identity, And Postcolonialism

The visual produces as well as represents culture, constituting (and constituted by) its relations of power and difference, so that cultures of everyday life are entwined with practices of representation. The continuous circulation and repetition of images presented as the norm or reality in visual media actively define social and cultural norms as fixed and natural. In the ways that people are depicted, vision is complicit with power and discipline through surveillance. Understanding visual representation as embodying and constituting ideologies (the system of thoughts and beliefs that determine the subjects action and behavior) shows how ways of investing meaning in the world are realized in visual representations.

The theorization and study of identities is one example of a significant area that has shown the potential of visual representation in understanding social and cultural phenomena (e.g., Hall 1997). Work has shown how racial, gendered, and other identities are visually represented, recycled, and contested. Visual discourses work to govern and empower particular understandings of a subject through their representation. Building on the work of Foucault (1977), identity theorists have examined visual representation as a part of the regulatory force of culture (sets of practices, cultural norms, meanings, and values) as they apply to the production of identities.

The cultural practices of looking and seeing are (like other social practices) organized around the founding principles of the articulation of difference . Looking at how representations attempt to fix difference offers a way of conceptualizing the complex relationship of power and representation. The practices of representation can be employed, for instance, to mark gender and racial difference. The “visible” signifiers of race and gender on the body can be called on to make these differences seem real and therefore true. The differences we can “see” appear to ground their “truth” beyond the social and historical construction of race and gender into nature and therefore to be unchangeable. The question of how the “other” is produced and reproduced through visual representation is central to postcolonial theories. The idea that discipline and control can be achieved through relations of looking and the knowledge and power that vision allows over what is seen is central to Foucault’s work and postcolonial theories.

Visual representations are, then, a discursive means by which a dominant group works to establish and maintain hegemonic power within a culture in which meaning is constantly reproduced and remade as signs are articulated and rearticulated. Images are thus a site of struggle for meaning , a site of power, and constitutive of society. Cultural production therefore has real political and ideological effects. As a result, visual representation has also been taken up as a potential tool for resistance and the remaking – reimaging – of society.

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Visual Representation

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Graphical representation

The concept of “representation” captures the signs that stand in for and take the place of something else [ 5 ]. Visual representation, in particular, refers to the special case when these signs are visual (as opposed to textual, mathematical, etc.). On the other hand, there is no limit on what may be (visually) represented, which may range from abstract concepts to concrete objects in the real world or data items.

In addition to the above, however, the term “representation” is often overloaded and used to imply the actual process of connecting the two worlds of the original items and of their representatives. Typically, the context determines quite clearly which of the two meanings is intended in each case, hence, the term is used for both without further explanation.

Underneath any visual representation lies a mapping between the set of items that are being represented and the set of visual elements that are used to represent them, i.e., to...

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The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works

• Maria Evagorou 1 ,
• Sibel Erduran 2 &
• Terhi Mäntylä 3  

International Journal of STEM Education volume  2 , Article number:  11 ( 2015 ) Cite this article

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The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects of scientific practices, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective.

This is a theoretical paper, and in order to argue about the role of visualization, we first present a case study, that of the discovery of the structure of DNA that highlights the epistemic components of visual information in science. The second case study focuses on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of the experimentation. Third, we trace a contemporary account from science focusing on the experimental practices and how reproducibility of experimental procedures can be reinforced through video data.

Conclusions

Our conclusions suggest that in teaching science, the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Furthermore, we suggest that is it essential to design curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication and reflect on these practices. Implications for teacher education include the need for teacher professional development programs to problematize the use of visual representations as epistemic objects that are part of scientific practices.

During the last decades, research and reform documents in science education across the world have been calling for an emphasis not only on the content but also on the processes of science (Bybee 2014 ; Eurydice 2012 ; Duschl and Bybee 2014 ; Osborne 2014 ; Schwartz et al. 2012 ), in order to make science accessible to the students and enable them to understand the epistemic foundation of science. Scientific practices, part of the process of science, are the cognitive and discursive activities that are targeted in science education to develop epistemic understanding and appreciation of the nature of science (Duschl et al. 2008 ) and have been the emphasis of recent reform documents in science education across the world (Achieve 2013 ; Eurydice 2012 ). With the term scientific practices, we refer to the processes that take place during scientific discoveries and include among others: asking questions, developing and using models, engaging in arguments, and constructing and communicating explanations (National Research Council 2012 ). The emphasis on scientific practices aims to move the teaching of science from knowledge to the understanding of the processes and the epistemic aspects of science. Additionally, by placing an emphasis on engaging students in scientific practices, we aim to help students acquire scientific knowledge in meaningful contexts that resemble the reality of scientific discoveries.

Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective. Specifically, the use of visual representations (i.e., photographs, diagrams, tables, charts) has been part of science and over the years has evolved with the new technologies (i.e., from drawings to advanced digital images and three dimensional models). Visualization makes it possible for scientists to interact with complex phenomena (Richards 2003 ), and they might convey important evidence not observable in other ways. Visual representations as a tool to support cognitive understanding in science have been studied extensively (i.e., Gilbert 2010 ; Wu and Shah 2004 ). Studies in science education have explored the use of images in science textbooks (i.e., Dimopoulos et al. 2003 ; Bungum 2008 ), students’ representations or models when doing science (i.e., Gilbert et al. 2008 ; Dori et al. 2003 ; Lehrer and Schauble 2012 ; Schwarz et al. 2009 ), and students’ images of science and scientists (i.e., Chambers 1983 ). Therefore, studies in the field of science education have been using the term visualization as “the formation of an internal representation from an external representation” (Gilbert et al. 2008 , p. 4) or as a tool for conceptual understanding for students.

In this paper, we do not refer to visualization as mental image, model, or presentation only (Gilbert et al. 2008 ; Philips et al. 2010 ) but instead focus on visual representations or visualization as epistemic objects. Specifically, we refer to visualization as a process for knowledge production and growth in science. In this respect, modeling is an aspect of visualization, but what we are focusing on with visualization is not on the use of model as a tool for cognitive understanding (Gilbert 2010 ; Wu and Shah 2004 ) but the on the process of modeling as a scientific practice which includes the construction and use of models, the use of other representations, the communication in the groups with the use of the visual representation, and the appreciation of the difficulties that the science phase in this process. Therefore, the purpose of this paper is to present through the history of science how visualization can be considered not only as a cognitive tool in science education but also as an epistemic object that can potentially support students to understand aspects of the nature of science.

Scientific practices and science education

According to the New Generation Science Standards (Achieve 2013 ), scientific practices refer to: asking questions and defining problems; developing and using models; planning and carrying out investigations; analyzing and interpreting data; using mathematical and computational thinking; constructing explanations and designing solutions; engaging in argument from evidence; and obtaining, evaluating, and communicating information. A significant aspect of scientific practices is that science learning is more than just about learning facts, concepts, theories, and laws. A fuller appreciation of science necessitates the understanding of the science relative to its epistemological grounding and the process that are involved in the production of knowledge (Hogan and Maglienti 2001 ; Wickman 2004 ).

The New Generation Science Standards is, among other changes, shifting away from science inquiry and towards the inclusion of scientific practices (Duschl and Bybee 2014 ; Osborne 2014 ). By comparing the abilities to do scientific inquiry (National Research Council 2000 ) with the set of scientific practices, it is evident that the latter is about engaging in the processes of doing science and experiencing in that way science in a more authentic way. Engaging in scientific practices according to Osborne ( 2014 ) “presents a more authentic picture of the endeavor that is science” (p.183) and also helps the students to develop a deeper understanding of the epistemic aspects of science. Furthermore, as Bybee ( 2014 ) argues, by engaging students in scientific practices, we involve them in an understanding of the nature of science and an understanding on the nature of scientific knowledge.

Science as a practice and scientific practices as a term emerged by the philosopher of science, Kuhn (Osborne 2014 ), refers to the processes in which the scientists engage during knowledge production and communication. The work that is followed by historians, philosophers, and sociologists of science (Latour 2011 ; Longino 2002 ; Nersessian 2008 ) revealed the scientific practices in which the scientists engage in and include among others theory development and specific ways of talking, modeling, and communicating the outcomes of science.

Visualization as an epistemic object

Schematic, pictorial symbols in the design of scientific instruments and analysis of the perceptual and functional information that is being stored in those images have been areas of investigation in philosophy of scientific experimentation (Gooding et al. 1993 ). The nature of visual perception, the relationship between thought and vision, and the role of reproducibility as a norm for experimental research form a central aspect of this domain of research in philosophy of science. For instance, Rothbart ( 1997 ) has argued that visualizations are commonplace in the theoretical sciences even if every scientific theory may not be defined by visualized models.

Visual representations (i.e., photographs, diagrams, tables, charts, models) have been used in science over the years to enable scientists to interact with complex phenomena (Richards 2003 ) and might convey important evidence not observable in other ways (Barber et al. 2006 ). Some authors (e.g., Ruivenkamp and Rip 2010 ) have argued that visualization is as a core activity of some scientific communities of practice (e.g., nanotechnology) while others (e.g., Lynch and Edgerton 1988 ) have differentiated the role of particular visualization techniques (e.g., of digital image processing in astronomy). Visualization in science includes the complex process through which scientists develop or produce imagery, schemes, and graphical representation, and therefore, what is of importance in this process is not only the result but also the methodology employed by the scientists, namely, how this result was produced. Visual representations in science may refer to objects that are believed to have some kind of material or physical existence but equally might refer to purely mental, conceptual, and abstract constructs (Pauwels 2006 ). More specifically, visual representations can be found for: (a) phenomena that are not observable with the eye (i.e., microscopic or macroscopic); (b) phenomena that do not exist as visual representations but can be translated as such (i.e., sound); and (c) in experimental settings to provide visual data representations (i.e., graphs presenting velocity of moving objects). Additionally, since science is not only about replicating reality but also about making it more understandable to people (either to the public or other scientists), visual representations are not only about reproducing the nature but also about: (a) functioning in helping solving a problem, (b) filling gaps in our knowledge, and (c) facilitating knowledge building or transfer (Lynch 2006 ).

Using or developing visual representations in the scientific practice can range from a straightforward to a complicated situation. More specifically, scientists can observe a phenomenon (i.e., mitosis) and represent it visually using a picture or diagram, which is quite straightforward. But they can also use a variety of complicated techniques (i.e., crystallography in the case of DNA studies) that are either available or need to be developed or refined in order to acquire the visual information that can be used in the process of theory development (i.e., Latour and Woolgar 1979 ). Furthermore, some visual representations need decoding, and the scientists need to learn how to read these images (i.e., radiologists); therefore, using visual representations in the process of science requires learning a new language that is specific to the medium/methods that is used (i.e., understanding an X-ray picture is different from understanding an MRI scan) and then communicating that language to other scientists and the public.

There are much intent and purposes of visual representations in scientific practices, as for example to make a diagnosis, compare, describe, and preserve for future study, verify and explore new territory, generate new data (Pauwels 2006 ), or present new methodologies. According to Latour and Woolgar ( 1979 ) and Knorr Cetina ( 1999 ), visual representations can be used either as primary data (i.e., image from a microscope). or can be used to help in concept development (i.e., models of DNA used by Watson and Crick), to uncover relationships and to make the abstract more concrete (graphs of sound waves). Therefore, visual representations and visual practices, in all forms, are an important aspect of the scientific practices in developing, clarifying, and transmitting scientific knowledge (Pauwels 2006 ).

Methods and Results: Merging Visualization and scientific practices in science

In this paper, we present three case studies that embody the working practices of scientists in an effort to present visualization as a scientific practice and present our argument about how visualization is a complex process that could include among others modeling and use of representation but is not only limited to that. The first case study explores the role of visualization in the construction of knowledge about the structure of DNA, using visuals as evidence. The second case study focuses on Faraday’s use of the lines of magnetic force and the visual reasoning leading to the theoretical development that was an inherent part of the experimentation. The third case study focuses on the current practices of scientists in the context of a peer-reviewed journal called the Journal of Visualized Experiments where the methodology is communicated through videotaped procedures. The three case studies represent the research interests of the three authors of this paper and were chosen to present how visualization as a practice can be involved in all stages of doing science, from hypothesizing and evaluating evidence (case study 1) to experimenting and reasoning (case study 2) to communicating the findings and methodology with the research community (case study 3), and represent in this way the three functions of visualization as presented by Lynch ( 2006 ). Furthermore, the last case study showcases how the development of visualization technologies has contributed to the communication of findings and methodologies in science and present in that way an aspect of current scientific practices. In all three cases, our approach is guided by the observation that the visual information is an integral part of scientific practices at the least and furthermore that they are particularly central in the scientific practices of science.

Case study 1: use visual representations as evidence in the discovery of DNA

The focus of the first case study is the discovery of the structure of DNA. The DNA was first isolated in 1869 by Friedrich Miescher, and by the late 1940s, it was known that it contained phosphate, sugar, and four nitrogen-containing chemical bases. However, no one had figured the structure of the DNA until Watson and Crick presented their model of DNA in 1953. Other than the social aspects of the discovery of the DNA, another important aspect was the role of visual evidence that led to knowledge development in the area. More specifically, by studying the personal accounts of Watson ( 1968 ) and Crick ( 1988 ) about the discovery of the structure of the DNA, the following main ideas regarding the role of visual representations in the production of knowledge can be identified: (a) The use of visual representations was an important part of knowledge growth and was often dependent upon the discovery of new technologies (i.e., better microscopes or better techniques in crystallography that would provide better visual representations as evidence of the helical structure of the DNA); and (b) Models (three-dimensional) were used as a way to represent the visual images (X-ray images) and connect them to the evidence provided by other sources to see whether the theory can be supported. Therefore, the model of DNA was built based on the combination of visual evidence and experimental data.

An example showcasing the importance of visual representations in the process of knowledge production in this case is provided by Watson, in his book The Double Helix (1968):

…since the middle of the summer Rosy [Rosalind Franklin] had had evidence for a new three-dimensional form of DNA. It occurred when the DNA 2molecules were surrounded by a large amount of water. When I asked what the pattern was like, Maurice went into the adjacent room to pick up a print of the new form they called the “B” structure. The instant I saw the picture, my mouth fell open and my pulse began to race. The pattern was unbelievably simpler than those previously obtained (A form). Moreover, the black cross of reflections which dominated the picture could arise only from a helical structure. With the A form the argument for the helix was never straightforward, and considerable ambiguity existed as to exactly which type of helical symmetry was present. With the B form however, mere inspection of its X-ray picture gave several of the vital helical parameters. (p. 167-169)

As suggested by Watson’s personal account of the discovery of the DNA, the photo taken by Rosalind Franklin (Fig.  1 ) convinced him that the DNA molecule must consist of two chains arranged in a paired helix, which resembles a spiral staircase or ladder, and on March 7, 1953, Watson and Crick finished and presented their model of the structure of DNA (Watson and Berry 2004 ; Watson 1968 ) which was based on the visual information provided by the X-ray image and their knowledge of chemistry.

X-ray chrystallography of DNA

In analyzing the visualization practice in this case study, we observe the following instances that highlight how the visual information played a role:

Asking questions and defining problems: The real world in the model of science can at some points only be observed through visual representations or representations, i.e., if we are using DNA as an example, the structure of DNA was only observable through the crystallography images produced by Rosalind Franklin in the laboratory. There was no other way to observe the structure of DNA, therefore the real world.

Analyzing and interpreting data: The images that resulted from crystallography as well as their interpretations served as the data for the scientists studying the structure of DNA.

Experimenting: The data in the form of visual information were used to predict the possible structure of the DNA.

Modeling: Based on the prediction, an actual three-dimensional model was prepared by Watson and Crick. The first model did not fit with the real world (refuted by Rosalind Franklin and her research group from King’s College) and Watson and Crick had to go through the same process again to find better visual evidence (better crystallography images) and create an improved visual model.

Example excerpts from Watson’s biography provide further evidence for how visualization practices were applied in the context of the discovery of DNA (Table  1 ).

In summary, by examining the history of the discovery of DNA, we showcased how visual data is used as scientific evidence in science, identifying in that way an aspect of the nature of science that is still unexplored in the history of science and an aspect that has been ignored in the teaching of science. Visual representations are used in many ways: as images, as models, as evidence to support or rebut a model, and as interpretations of reality.

Case study 2: applying visual reasoning in knowledge production, the example of the lines of magnetic force

The focus of this case study is on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of this experimentation (Gooding 2006 ). Faraday’s articles or notebooks do not include mathematical formulations; instead, they include images and illustrations from experimental devices and setups to the recapping of his theoretical ideas (Nersessian 2008 ). According to Gooding ( 2006 ), “Faraday’s visual method was designed not to copy apparent features of the world, but to analyse and replicate them” (2006, p. 46).

The lines of force played a central role in Faraday’s research on electricity and magnetism and in the development of his “field theory” (Faraday 1852a ; Nersessian 1984 ). Before Faraday, the experiments with iron filings around magnets were known and the term “magnetic curves” was used for the iron filing patterns and also for the geometrical constructs derived from the mathematical theory of magnetism (Gooding et al. 1993 ). However, Faraday used the lines of force for explaining his experimental observations and in constructing the theory of forces in magnetism and electricity. Examples of Faraday’s different illustrations of lines of magnetic force are given in Fig.  2 . Faraday gave the following experiment-based definition for the lines of magnetic forces:

a Iron filing pattern in case of bar magnet drawn by Faraday (Faraday 1852b , Plate IX, p. 158, Fig. 1), b Faraday’s drawing of lines of magnetic force in case of cylinder magnet, where the experimental procedure, knife blade showing the direction of lines, is combined into drawing (Faraday, 1855, vol. 1, plate 1)

A line of magnetic force may be defined as that line which is described by a very small magnetic needle, when it is so moved in either direction correspondent to its length, that the needle is constantly a tangent to the line of motion; or it is that line along which, if a transverse wire be moved in either direction, there is no tendency to the formation of any current in the wire, whilst if moved in any other direction there is such a tendency; or it is that line which coincides with the direction of the magnecrystallic axis of a crystal of bismuth, which is carried in either direction along it. The direction of these lines about and amongst magnets and electric currents, is easily represented and understood, in a general manner, by the ordinary use of iron filings. (Faraday 1852a , p. 25 (3071))

The definition describes the connection between the experiments and the visual representation of the results. Initially, the lines of force were just geometric representations, but later, Faraday treated them as physical objects (Nersessian 1984 ; Pocovi and Finlay 2002 ):

I have sometimes used the term lines of force so vaguely, as to leave the reader doubtful whether I intended it as a merely representative idea of the forces, or as the description of the path along which the power was continuously exerted. … wherever the expression line of force is taken simply to represent the disposition of forces, it shall have the fullness of that meaning; but that wherever it may seem to represent the idea of the physical mode of transmission of the force, it expresses in that respect the opinion to which I incline at present. The opinion may be erroneous, and yet all that relates or refers to the disposition of the force will remain the same. (Faraday, 1852a , p. 55-56 (3075))

He also felt that the lines of force had greater explanatory power than the dominant theory of action-at-a-distance:

Now it appears to me that these lines may be employed with great advantage to represent nature, condition, direction and comparative amount of the magnetic forces; and that in many cases they have, to the physical reasoned at least, a superiority over that method which represents the forces as concentrated in centres of action… (Faraday, 1852a , p. 26 (3074))

For giving some insight to Faraday’s visual reasoning as an epistemic practice, the following examples of Faraday’s studies of the lines of magnetic force (Faraday 1852a , 1852b ) are presented:

(a) Asking questions and defining problems: The iron filing patterns formed the empirical basis for the visual model: 2D visualization of lines of magnetic force as presented in Fig.  2 . According to Faraday, these iron filing patterns were suitable for illustrating the direction and form of the magnetic lines of force (emphasis added):

It must be well understood that these forms give no indication by their appearance of the relative strength of the magnetic force at different places, inasmuch as the appearance of the lines depends greatly upon the quantity of filings and the amount of tapping; but the direction and forms of these lines are well given, and these indicate, in a considerable degree, the direction in which the forces increase and diminish . (Faraday 1852b , p.158 (3237))

Despite being static and two dimensional on paper, the lines of magnetic force were dynamical (Nersessian 1992 , 2008 ) and three dimensional for Faraday (see Fig.  2 b). For instance, Faraday described the lines of force “expanding”, “bending,” and “being cut” (Nersessian 1992 ). In Fig.  2 b, Faraday has summarized his experiment (bar magnet and knife blade) and its results (lines of force) in one picture.

(b) Analyzing and interpreting data: The model was so powerful for Faraday that he ended up thinking them as physical objects (e.g., Nersessian 1984 ), i.e., making interpretations of the way forces act. Of course, he made a lot of experiments for showing the physical existence of the lines of force, but he did not succeed in it (Nersessian 1984 ). The following quote illuminates Faraday’s use of the lines of force in different situations:

The study of these lines has, at different times, been greatly influential in leading me to various results, which I think prove their utility as well as fertility. Thus, the law of magneto-electric induction; the earth’s inductive action; the relation of magnetism and light; diamagnetic action and its law, and magnetocrystallic action, are the cases of this kind… (Faraday 1852a , p. 55 (3174))

(c) Experimenting: In Faraday's case, he used a lot of exploratory experiments; in case of lines of magnetic force, he used, e.g., iron filings, magnetic needles, or current carrying wires (see the quote above). The magnetic field is not directly observable and the representation of lines of force was a visual model, which includes the direction, form, and magnitude of field.

(d) Modeling: There is no denying that the lines of magnetic force are visual by nature. Faraday’s views of lines of force developed gradually during the years, and he applied and developed them in different contexts such as electromagnetic, electrostatic, and magnetic induction (Nersessian 1984 ). An example of Faraday’s explanation of the effect of the wire b’s position to experiment is given in Fig.  3 . In Fig.  3 , few magnetic lines of force are drawn, and in the quote below, Faraday is explaining the effect using these magnetic lines of force (emphasis added):

Picture of an experiment with different arrangements of wires ( a , b’ , b” ), magnet, and galvanometer. Note the lines of force drawn around the magnet. (Faraday 1852a , p. 34)

It will be evident by inspection of Fig. 3 , that, however the wires are carried away, the general result will, according to the assumed principles of action, be the same; for if a be the axial wire, and b’, b”, b”’ the equatorial wire, represented in three different positions, whatever magnetic lines of force pass across the latter wire in one position, will also pass it in the other, or in any other position which can be given to it. The distance of the wire at the place of intersection with the lines of force, has been shown, by the experiments (3093.), to be unimportant. (Faraday 1852a , p. 34 (3099))

In summary, by examining the history of Faraday’s use of lines of force, we showed how visual imagery and reasoning played an important part in Faraday’s construction and representation of his “field theory”. As Gooding has stated, “many of Faraday’s sketches are far more that depictions of observation, they are tools for reasoning with and about phenomena” (2006, p. 59).

Case study 3: visualizing scientific methods, the case of a journal

The focus of the third case study is the Journal of Visualized Experiments (JoVE) , a peer-reviewed publication indexed in PubMed. The journal devoted to the publication of biological, medical, chemical, and physical research in a video format. The journal describes its history as follows:

JoVE was established as a new tool in life science publication and communication, with participation of scientists from leading research institutions. JoVE takes advantage of video technology to capture and transmit the multiple facets and intricacies of life science research. Visualization greatly facilitates the understanding and efficient reproduction of both basic and complex experimental techniques, thereby addressing two of the biggest challenges faced by today's life science research community: i) low transparency and poor reproducibility of biological experiments and ii) time and labor-intensive nature of learning new experimental techniques. ( http://www.jove.com/ )

By examining the journal content, we generate a set of categories that can be considered as indicators of relevance and significance in terms of epistemic practices of science that have relevance for science education. For example, the quote above illustrates how scientists view some norms of scientific practice including the norms of “transparency” and “reproducibility” of experimental methods and results, and how the visual format of the journal facilitates the implementation of these norms. “Reproducibility” can be considered as an epistemic criterion that sits at the heart of what counts as an experimental procedure in science:

Investigating what should be reproducible and by whom leads to different types of experimental reproducibility, which can be observed to play different roles in experimental practice. A successful application of the strategy of reproducing an experiment is an achievement that may depend on certain isiosyncratic aspects of a local situation. Yet a purely local experiment that cannot be carried out by other experimenters and in other experimental contexts will, in the end be unproductive in science. (Sarkar and Pfeifer 2006 , p.270)

We now turn to an article on “Elevated Plus Maze for Mice” that is available for free on the journal website ( http://www.jove.com/video/1088/elevated-plus-maze-for-mice ). The purpose of this experiment was to investigate anxiety levels in mice through behavioral analysis. The journal article consists of a 9-min video accompanied by text. The video illustrates the handling of the mice in soundproof location with less light, worksheets with characteristics of mice, computer software, apparatus, resources, setting up the computer software, and the video recording of mouse behavior on the computer. The authors describe the apparatus that is used in the experiment and state how procedural differences exist between research groups that lead to difficulties in the interpretation of results:

The apparatus consists of open arms and closed arms, crossed in the middle perpendicularly to each other, and a center area. Mice are given access to all of the arms and are allowed to move freely between them. The number of entries into the open arms and the time spent in the open arms are used as indices of open space-induced anxiety in mice. Unfortunately, the procedural differences that exist between laboratories make it difficult to duplicate and compare results among laboratories.

The authors’ emphasis on the particularity of procedural context echoes in the observations of some philosophers of science:

It is not just the knowledge of experimental objects and phenomena but also their actual existence and occurrence that prove to be dependent on specific, productive interventions by the experimenters” (Sarkar and Pfeifer 2006 , pp. 270-271)

The inclusion of a video of the experimental procedure specifies what the apparatus looks like (Fig.  4 ) and how the behavior of the mice is captured through video recording that feeds into a computer (Fig.  5 ). Subsequently, a computer software which captures different variables such as the distance traveled, the number of entries, and the time spent on each arm of the apparatus. Here, there is visual information at different levels of representation ranging from reconfiguration of raw video data to representations that analyze the data around the variables in question (Fig.  6 ). The practice of levels of visual representations is not particular to the biological sciences. For instance, they are commonplace in nanotechnological practices:

Visual illustration of apparatus

Video processing of experimental set-up

Computer software for video input and variable recording

In the visualization processes, instruments are needed that can register the nanoscale and provide raw data, which needs to be transformed into images. Some Imaging Techniques have software incorporated already where this transformation automatically takes place, providing raw images. Raw data must be translated through the use of Graphic Software and software is also used for the further manipulation of images to highlight what is of interest to capture the (inferred) phenomena -- and to capture the reader. There are two levels of choice: Scientists have to choose which imaging technique and embedded software to use for the job at hand, and they will then have to follow the structure of the software. Within such software, there are explicit choices for the scientists, e.g. about colour coding, and ways of sharpening images. (Ruivenkamp and Rip 2010 , pp.14–15)

On the text that accompanies the video, the authors highlight the role of visualization in their experiment:

Visualization of the protocol will promote better understanding of the details of the entire experimental procedure, allowing for standardization of the protocols used in different laboratories and comparisons of the behavioral phenotypes of various strains of mutant mice assessed using this test.

The software that takes the video data and transforms it into various representations allows the researchers to collect data on mouse behavior more reliably. For instance, the distance traveled across the arms of the apparatus or the time spent on each arm would have been difficult to observe and record precisely. A further aspect to note is how the visualization of the experiment facilitates control of bias. The authors illustrate how the olfactory bias between experimental procedures carried on mice in sequence is avoided by cleaning the equipment.

Our discussion highlights the role of visualization in science, particularly with respect to presenting visualization as part of the scientific practices. We have used case studies from the history of science highlighting a scientist’s account of how visualization played a role in the discovery of DNA and the magnetic field and from a contemporary illustration of a science journal’s practices in incorporating visualization as a way to communicate new findings and methodologies. Our implicit aim in drawing from these case studies was the need to align science education with scientific practices, particularly in terms of how visual representations, stable or dynamic, can engage students in the processes of science and not only to be used as tools for cognitive development in science. Our approach was guided by the notion of “knowledge-as-practice” as advanced by Knorr Cetina ( 1999 ) who studied scientists and characterized their knowledge as practice, a characterization which shifts focus away from ideas inside scientists’ minds to practices that are cultural and deeply contextualized within fields of science. She suggests that people working together can be examined as epistemic cultures whose collective knowledge exists as practice.

It is important to stress, however, that visual representations are not used in isolation, but are supported by other types of evidence as well, or other theories (i.e., in order to understand the helical form of DNA, or the structure, chemistry knowledge was needed). More importantly, this finding can also have implications when teaching science as argument (e.g., Erduran and Jimenez-Aleixandre 2008 ), since the verbal evidence used in the science classroom to maintain an argument could be supported by visual evidence (either a model, representation, image, graph, etc.). For example, in a group of students discussing the outcomes of an introduced species in an ecosystem, pictures of the species and the ecosystem over time, and videos showing the changes in the ecosystem, and the special characteristics of the different species could serve as visual evidence to help the students support their arguments (Evagorou et al. 2012 ). Therefore, an important implication for the teaching of science is the use of visual representations as evidence in the science curriculum as part of knowledge production. Even though studies in the area of science education have focused on the use of models and modeling as a way to support students in the learning of science (Dori et al. 2003 ; Lehrer and Schauble 2012 ; Mendonça and Justi 2013 ; Papaevripidou et al. 2007 ) or on the use of images (i.e., Korfiatis et al. 2003 ), with the term using visuals as evidence, we refer to the collection of all forms of visuals and the processes involved.

Another aspect that was identified through the case studies is that of the visual reasoning (an integral part of Faraday’s investigations). Both the verbalization and visualization were part of the process of generating new knowledge (Gooding 2006 ). Even today, most of the textbooks use the lines of force (or just field lines) as a geometrical representation of field, and the number of field lines is connected to the quantity of flux. Often, the textbooks use the same kind of visual imagery than in what is used by scientists. However, when using images, only certain aspects or features of the phenomena or data are captured or highlighted, and often in tacit ways. Especially in textbooks, the process of producing the image is not presented and instead only the product—image—is left. This could easily lead to an idea of images (i.e., photos, graphs, visual model) being just representations of knowledge and, in the worse case, misinterpreted representations of knowledge as the results of Pocovi and Finlay ( 2002 ) in case of electric field lines show. In order to avoid this, the teachers should be able to explain how the images are produced (what features of phenomena or data the images captures, on what ground the features are chosen to that image, and what features are omitted); in this way, the role of visualization in knowledge production can be made “visible” to students by engaging them in the process of visualization.

The implication of these norms for science teaching and learning is numerous. The classroom contexts can model the generation, sharing and evaluation of evidence, and experimental procedures carried out by students, thereby promoting not only some contemporary cultural norms in scientific practice but also enabling the learning of criteria, standards, and heuristics that scientists use in making decisions on scientific methods. As we have demonstrated with the three case studies, visual representations are part of the process of knowledge growth and communication in science, as demonstrated with two examples from the history of science and an example from current scientific practices. Additionally, visual information, especially with the use of technology is a part of students’ everyday lives. Therefore, we suggest making use of students’ knowledge and technological skills (i.e., how to produce their own videos showing their experimental method or how to identify or provide appropriate visual evidence for a given topic), in order to teach them the aspects of the nature of science that are often neglected both in the history of science and the design of curriculum. Specifically, what we suggest in this paper is that students should actively engage in visualization processes in order to appreciate the diverse nature of doing science and engage in authentic scientific practices.

However, as a word of caution, we need to distinguish the products and processes involved in visualization practices in science:

If one considers scientific representations and the ways in which they can foster or thwart our understanding, it is clear that a mere object approach, which would devote all attention to the representation as a free-standing product of scientific labor, is inadequate. What is needed is a process approach: each visual representation should be linked with its context of production (Pauwels 2006 , p.21).

The aforementioned suggests that the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Therefore, an implication for the teaching of science includes designing curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication (as presented in the three case studies) and reflect on these practices (Ryu et al. 2015 ).

Finally, a question that arises from including visualization in science education, as well as from including scientific practices in science education is whether teachers themselves are prepared to include them as part of their teaching (Bybee 2014 ). Teacher preparation programs and teacher education have been critiqued, studied, and rethought since the time they emerged (Cochran-Smith 2004 ). Despite the years of history in teacher training and teacher education, the debate about initial teacher training and its content still pertains in our community and in policy circles (Cochran-Smith 2004 ; Conway et al. 2009 ). In the last decades, the debate has shifted from a behavioral view of learning and teaching to a learning problem—focusing on that way not only on teachers’ knowledge, skills, and beliefs but also on making the connection of the aforementioned with how and if pupils learn (Cochran-Smith 2004 ). The Science Education in Europe report recommended that “Good quality teachers, with up-to-date knowledge and skills, are the foundation of any system of formal science education” (Osborne and Dillon 2008 , p.9).

However, questions such as what should be the emphasis on pre-service and in-service science teacher training, especially with the new emphasis on scientific practices, still remain unanswered. As Bybee ( 2014 ) argues, starting from the new emphasis on scientific practices in the NGSS, we should consider teacher preparation programs “that would provide undergraduates opportunities to learn the science content and practices in contexts that would be aligned with their future work as teachers” (p.218). Therefore, engaging pre- and in-service teachers in visualization as a scientific practice should be one of the purposes of teacher preparation programs.

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ME carried out the introductory literature review, the analysis of the first case study, and drafted the manuscript. SE carried out the analysis of the third case study and contributed towards the “Conclusions” section of the manuscript. TM carried out the second case study. All authors read and approved the final manuscript.

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Evagorou, M., Erduran, S. & Mäntylä, T. The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works. IJ STEM Ed 2 , 11 (2015). https://doi.org/10.1186/s40594-015-0024-x

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• Visual representations
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Initial Thoughts

Perspectives & resources, what is high-quality mathematics instruction and why is it important.

• Page 1: The Importance of High-Quality Mathematics Instruction
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What evidence-based mathematics practices can teachers employ?

• Page 4: Explicit, Systematic Instruction

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• Page 10: Credits

Research Shows

• Students who use accurate visual representations are six times more likely to correctly solve mathematics problems than are students who do not use them. However, students who use inaccurate visual representations are less likely to correctly solve mathematics problems than those who do not use visual representations at all. (Boonen, van Wesel, Jolles, & van der Schoot, 2014)
• Students with a learning disability (LD) often do not create accurate visual representations or use them strategically to solve problems. Teaching students to systematically use a visual representation to solve word problems has led to substantial improvements in math achievement for students with learning disabilities. (van Garderen, Scheuermann, & Jackson, 2012; van Garderen, Scheuermann, & Poch, 2014)
• Students who use visual representations to solve word problems are more likely to solve the problems accurately. This was equally true for students who had LD, were low-achieving, or were average-achieving. (Krawec, 2014)

Visual representations are flexible; they can be used across grade levels and types of math problems. They can be used by teachers to teach mathematics facts and by students to learn mathematics content. Visual representations can take a number of forms. Click on the links below to view some of the visual representations most commonly used by teachers and students.

How does this practice align?

High-leverage practice (hlp).

• HLP15 : Provide scaffolded supports

CCSSM: Standards for Mathematical Practice

• MP1 : Make sense of problems and persevere in solving them.

Number Lines

Definition : A straight line that shows the order of and the relation between numbers.

Common Uses : addition, subtraction, counting

Strip Diagrams

Definition : A bar divided into rectangles that accurately represent quantities noted in the problem.

Common Uses : addition, fractions, proportions, ratios

Definition : Simple drawings of concrete or real items (e.g., marbles, trucks).

Common Uses : counting, addition, subtraction, multiplication, division

Graphs/Charts

Definition : Drawings that depict information using lines, shapes, and colors.

Common Uses : comparing numbers, statistics, ratios, algebra

Graphic Organizers

Definition : Visual that assists students in remembering and organizing information, as well as depicting the relationships between ideas (e.g., word webs, tables, Venn diagrams).

Common Uses : algebra, geometry

Before they can solve problems, however, students must first know what type of visual representation to create and use for a given mathematics problem. Some students—specifically, high-achieving students, gifted students—do this automatically, whereas others need to be explicitly taught how. This is especially the case for students who struggle with mathematics and those with mathematics learning disabilities. Without explicit, systematic instruction on how to create and use visual representations, these students often create visual representations that are disorganized or contain incorrect or partial information. Consider the examples below.

Elementary Example

Mrs. Aldridge ask her first-grade students to add 2 + 4 by drawing dots.

Notice that Talia gets the correct answer. However, because Colby draws his dots in haphazard fashion, he fails to count all of them and consequently arrives at the wrong solution.

High School Example

Mr. Huang asks his students to solve the following word problem:

The flagpole needs to be replaced. The school would like to replace it with the same size pole. When Juan stands 11 feet from the base of the pole, the angle of elevation from Juan’s feet to the top of the pole is 70 degrees. How tall is the pole?

Compare the drawings below created by Brody and Zoe to represent this problem. Notice that Brody drew an accurate representation and applied the correct strategy. In contrast, Zoe drew a picture with partially correct information. The 11 is in the correct place, but the 70° is not. As a result of her inaccurate representation, Zoe is unable to move forward and solve the problem. However, given an accurate representation developed by someone else, Zoe is more likely to solve the problem correctly.

Manipulatives

Some students will not be able to grasp mathematics skills and concepts using only the types of visual representations noted in the table above. Very young children and students who struggle with mathematics often require different types of visual representations known as manipulatives. These concrete, hands-on materials and objects—for example, an abacus or coins—help students to represent the mathematical idea they are trying to learn or the problem they are attempting to solve. Manipulatives can help students develop a conceptual understanding of mathematical topics. (For the purpose of this module, the term concrete objects refers to manipulatives and the term visual representations refers to schematic diagrams.)

It is important that the teacher make explicit the connection between the concrete object and the abstract concept being taught. The goal is for the student to eventually understand the concepts and procedures without the use of manipulatives. For secondary students who struggle with mathematics, teachers should show the abstract along with the concrete or visual representation and explicitly make the connection between them.

A move from concrete objects or visual representations to using abstract equations can be difficult for some students. One strategy teachers can use to help students systematically transition among concrete objects, visual representations, and abstract equations is the Concrete-Representational-Abstract (CRA) framework.

If you would like to learn more about this framework, click here.

Concrete-Representational-Abstract Framework

• Concrete —Students interact and manipulate three-dimensional objects, for example algebra tiles or other algebra manipulatives with representations of variables and units.
• Representational — Students use two-dimensional drawings to represent problems. These pictures may be presented to them by the teacher, or through the curriculum used in the class, or students may draw their own representation of the problem.
• Abstract — Students solve problems with numbers, symbols, and words without any concrete or representational assistance.

CRA is effective across all age levels and can assist students in learning concepts, procedures, and applications. When implementing each component, teachers should use explicit, systematic instruction and continually monitor student work to assess their understanding, asking them questions about their thinking and providing clarification as needed. Concrete and representational activities must reflect the actual process of solving the problem so that students are able to generalize the process to solve an abstract equation. The illustration below highlights each of these components.

For Your Information

One promising practice for moving secondary students with mathematics difficulties or disabilities from the use of manipulatives and visual representations to the abstract equation quickly is the CRA-I strategy . In this modified version of CRA, the teacher simultaneously presents the content using concrete objects, visual representations of the concrete objects, and the abstract equation. Studies have shown that this framework is effective for teaching algebra to this population of students (Strickland & Maccini, 2012; Strickland & Maccini, 2013; Strickland, 2017).

Kim Paulsen discusses the benefits of manipulatives and a number of things to keep in mind when using them (time: 2:35).

Kim Paulsen, EdD Associate Professor, Special Education Vanderbilt University

View Transcript

Transcript: Kim Paulsen, EdD

Manipulatives are a great way of helping kids understand conceptually. The use of manipulatives really helps students see that conceptually, and it clicks a little more with them. Some of the things, though, that we need to remember when we’re using manipulatives is that it is important to give students a little bit of free time when you’re using a new manipulative so that they can just explore with them. We need to have specific rules for how to use manipulatives, that they aren’t toys, that they really are learning materials, and how students pick them up, how they put them away, the right time to use them, and making sure that they’re not distracters while we’re actually doing the presentation part of the lesson. One of the important things is that we don’t want students to memorize the algorithm or the procedures while they’re using the manipulatives. It really is just to help them understand conceptually. That doesn’t mean that kids are automatically going to understand conceptually or be able to make that bridge between using the concrete manipulatives into them being able to solve the problems. For some kids, it is difficult to use the manipulatives. That’s not how they learn, and so we don’t want to force kids to have to use manipulatives if it’s not something that is helpful for them. So we have to remember that manipulatives are one way to think about teaching math.

I think part of the reason that some teachers don’t use them is because it takes a lot of time, it takes a lot of organization, and they also feel that students get too reliant on using manipulatives. One way to think about using manipulatives is that you do it a couple of lessons when you’re teaching a new concept, and then take those away so that students are able to do just the computation part of it. It is true we can’t walk around life with manipulatives in our hands. And I think one of the other reasons that a lot of schools or teachers don’t use manipulatives is because they’re very expensive. And so it’s very helpful if all of the teachers in the school can pool resources and have a manipulative room where teachers can go check out manipulatives so that it’s not so expensive. Teachers have to know how to use them, and that takes a lot of practice.

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What is Data Visualization and Why is It Important?

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

This practice is crucial in the data science process, as it helps to make data more understandable and actionable for a wide range of users, from business professionals to data scientists.

Table of Content

What is Data Visualization?

Why is data visualization important, 1. data visualization discovers the trends in data, 2. data visualization provides a perspective on the data, 3. data visualization puts the data into the correct context, 4. data visualization saves time, 5. data visualization tells a data story, types of data visualization techniques, tools for visualization of data, advantages and disadvantages of data visualization, best practices for visualization data, use-cases and applications of data visualization.

Data visualization translates complex data sets into visual formats that are easier for the human brain to comprehend. This can include a variety of visual tools such as:

• Charts : Bar charts, line charts, pie charts, etc.
• Graphs : Scatter plots, histograms, etc.
• Maps : Geographic maps, heat maps, etc.
• Dashboards : Interactive platforms that combine multiple visualizations.

The primary goal of data visualization is to make data more accessible and easier to interpret, allowing users to identify patterns, trends, and outliers quickly. This is particularly important in the context of big data, where the sheer volume of information can be overwhelming without effective visualization techniques.

Types of Data for Visualization

Performing accurate visualization of data is very critical to market research where both numerical and categorical data can be visualized, which helps increase the impact of insights and also helps in reducing the risk of analysis paralysis. So, data visualization is categorized into the following categories:

• Numerical Data 
• Categorical Data

Let’s understand the visualization of data via a diagram with its all categories.

To read more on this refer to: Categories of Data Visualization

Let’s take an example. Suppose you compile visualization data of the company’s profits from 2013 to 2023 and create a line chart. It would be very easy to see the line going constantly up with a drop in just 2018. So you can observe in a second that the company has had continuous profits in all the years except a loss in 2018.

It would not be that easy to get this information so fast from a data table. This is just one demonstration of the usefulness of data visualization. Let’s see some more reasons why visualization of data is so important.

The most important thing that data visualization does is discover the trends in data. After all, it is much easier to observe data trends when all the data is laid out in front of you in a visual form as compared to data in a table. For example, the screenshot below on visualization on Tableau demonstrates the sum of sales made by each customer in descending order. However, the color red denotes loss while grey denotes profits. So it is very easy to observe from this visualization that even though some customers may have huge sales, they are still at a loss. This would be very difficult to observe from a table.

Visualizing Data provides a perspective on data by showing its meaning in the larger scheme of things. It demonstrates how particular data references stand concerning the overall data picture. In the data visualization below, the data between sales and profit provides a data perspective concerning these two measures. It also demonstrates that there are very few sales above 12K and higher sales do not necessarily mean a higher profit.

It isn’t easy to understand the context of the data with data visualization. Since context provides the whole circumstances of the data, it is very difficult to grasp by just reading numbers in a table. In the below data visualization on Tableau , a TreeMap is used to demonstrate the number of sales in each region of the United States. It is very easy to understand from this data visualization that California has the largest number of sales out of the total number since the rectangle for California is the largest. But this information is not easy to understand outside of context without visualizing data.

It is definitely faster to gather some insights from the data using data visualization rather than just studying a chart. In the screenshot below on Tableau, it is very easy to identify the states that have suffered a net loss rather than a profit. This is because all the cells with a loss are coloured red using a heat map, so it is obvious states have suffered a loss. Compare this to a normal table where you would need to check each cell to see if it has a negative value to determine a loss. Visualizing Data can save a lot of time in this situation!

Data visualization is also a medium to tell a data story to the viewers. The visualization can be used to present the data facts in an easy-to-understand form while telling a story and leading the viewers to an inevitable conclusion. This data story, like any other type of story, should have a good beginning, a basic plot, and an ending that it is leading towards. For example, if a data analyst has to craft a data visualization for company executives detailing the profits of various products, then the data story can start with the profits and losses of multiple products and move on to recommendations on how to tackle the losses.

To find out more points please refer to this article: Why is Data Visualization so Important?

Now, that we have understood the basics of Data Visualization, along with its importance, now will be discussing the Advantages, Disadvantages and Data Science Pipeline (along with the diagram) which will help you to understand how data is compiled through various checkpoints.

Various types of visualizations cater to diverse data sets and analytical goals.

• Bar Charts: Ideal for comparing categorical data or displaying frequencies, bar charts offer a clear visual representation of values.
• Line Charts: Perfect for illustrating trends over time, line charts connect data points to reveal patterns and fluctuations.
• Pie Charts: Efficient for displaying parts of a whole, pie charts offer a simple way to understand proportions and percentages.
• Scatter Plots: Showcase relationships between two variables, identifying patterns and outliers through scattered data points.
• Histograms: Depict the distribution of a continuous variable, providing insights into the underlying data patterns.
• Heatmaps: Visualize complex data sets through color-coding, emphasizing variations and correlations in a matrix.
• Box Plots: Unveil statistical summaries such as median, quartiles, and outliers, aiding in data distribution analysis.
• Area Charts: Similar to line charts but with the area under the line filled, these charts accentuate cumulative data patterns.
• Bubble Charts: Enhance scatter plots by introducing a third dimension through varying bubble sizes, revealing additional insights.
• Treemaps: Efficiently represent hierarchical data structures, breaking down categories into nested rectangles.
• Violin Plots : Violin plots combine aspects of box plots and kernel density plots, providing a detailed representation of the distribution of data.
• Word Clouds : Word clouds are visual representations of text data where words are sized based on their frequency.
• 3D Surface Plots : 3D surface plots visualize three-dimensional data, illustrating how a response variable changes in relation to two predictor variables.
• Network Graphs : Network graphs represent relationships between entities using nodes and edges. They are useful for visualizing connections in complex systems, such as social networks, transportation networks, or organizational structures.
• Sankey Diagrams : Sankey diagrams visualize flow and quantity relationships between multiple entities. Often used in process engineering or energy flow analysis.

Visualization of data not only simplifies complex information but also enhances decision-making processes. Choosing the right type of visualization helps to unveil hidden patterns and trends within the data, making informed and impactful conclusions.

The following are the 10 best Data Visualization Tools

• Zoho Analytics
• IBM Cognos Analytics
• Microsoft Power BI
• SAP Analytics Cloud

To  find out more about these tools please refer to this article: Best Data Visualization Tools

Advantages of Data Visualization:

• Enhanced Comparison: Visualizing performances of two elements or scenarios streamlines analysis, saving time compared to traditional data examination.
• Improved Methodology: Representing data graphically offers a superior understanding of situations, exemplified by tools like Google Trends illustrating industry trends in graphical forms.
• Efficient Data Sharing: Visual data presentation facilitates effective communication, making information more digestible and engaging compared to sharing raw data.
• Sales Analysis: Data visualization aids sales professionals in comprehending product sales trends, identifying influencing factors through tools like heat maps, and understanding customer types, geography impacts, and repeat customer behaviors.
• Identifying Event Relations: Discovering correlations between events helps businesses understand external factors affecting their performance, such as online sales surges during festive seasons.
• Exploring Opportunities and Trends: Data visualization empowers business leaders to uncover patterns and opportunities within vast datasets, enabling a deeper understanding of customer behaviors and insights into emerging business trends.

Disadvantages of Data Visualization:

• Can be time-consuming: Creating visualizations can be a time-consuming process, especially when dealing with large and complex datasets.
• Can be misleading: While data visualization can help identify patterns and relationships in data, it can also be misleading if not done correctly. Visualizations can create the impression of patterns or trends that may not exist, leading to incorrect conclusions and poor decision-making.
• Can be difficult to interpret: Some types of visualizations, such as those that involve 3D or interactive elements, can be difficult to interpret and understand.
• May not be suitable for all types of data: Certain types of data, such as text or audio data, may not lend themselves well to visualization. In these cases, alternative methods of analysis may be more appropriate.
• May not be accessible to all users: Some users may have visual impairments or other disabilities that make it difficult or impossible for them to interpret visualizations. In these cases, alternative methods of presenting data may be necessary to ensure accessibility.

Effective data visualization is crucial for conveying insights accurately. Follow these best practices to create compelling and understandable visualizations:

• Audience-Centric Approach: Tailor visualizations to your audience’s knowledge level, ensuring clarity and relevance. Consider their familiarity with data interpretation and adjust the complexity of visual elements accordingly.
• Design Clarity and Consistency: Choose appropriate chart types, simplify visual elements, and maintain a consistent color scheme and legible fonts. This ensures a clear, cohesive, and easily interpretable visualization.
• Contextual Communication: Provide context through clear labels, titles, annotations, and acknowledgments of data sources. This helps viewers understand the significance of the information presented and builds transparency and credibility.
• Engaging and Accessible Design: Design interactive features thoughtfully, ensuring they enhance comprehension. Additionally, prioritize accessibility by testing visualizations for responsiveness and accommodating various audience needs, fostering an inclusive and engaging experience.

1. Business Intelligence and Reporting

In the realm of Business Intelligence and Reporting, organizations leverage sophisticated tools to enhance decision-making processes. This involves the implementation of comprehensive dashboards designed for tracking key performance indicators (KPIs) and essential business metrics. Additionally, businesses engage in thorough trend analysis to discern patterns and anomalies within sales, revenue, and other critical datasets. These visual insights play a pivotal role in facilitating strategic decision-making, empowering stakeholders to respond promptly to market dynamics.

2. Financial Analysis

Financial Analysis in the corporate landscape involves the utilization of visual representations to aid in investment decision-making. Visualizing stock prices and market trends provides valuable insights for investors. Furthermore, organizations conduct comparative analyses of budgeted versus actual expenditures, gaining a comprehensive understanding of financial performance. Visualizations of cash flow and financial statements contribute to a clearer assessment of overall financial health, aiding in the formulation of robust financial strategies.

3. Healthcare

Within the Healthcare sector, the adoption of visualizations is instrumental in conveying complex information. Visual representations are employed to communicate patient outcomes and assess treatment efficacy, fostering a more accessible understanding for healthcare professionals and stakeholders. Moreover, visual depictions of disease spread and epidemiological data are critical in supporting public health efforts. Through visual analytics, healthcare organizations achieve efficient allocation and utilization of resources, ensuring optimal delivery of healthcare services.

4. Marketing and Sales

In the domain of Marketing and Sales, data visualization becomes a powerful tool for understanding customer behavior. Segmentation and behavior analysis are facilitated through visually intuitive charts, providing insights that inform targeted marketing strategies. Conversion funnel visualizations offer a comprehensive view of the customer journey, enabling organizations to optimize their sales processes. Visual analytics of social media engagement and campaign performance further enhance marketing strategies, allowing for more effective and targeted outreach.

5. Human Resources

Human Resources departments leverage data visualization to streamline processes and enhance workforce management. The development of employee performance dashboards facilitates efficient HR operations. Workforce demographics and diversity metrics are visually represented, supporting inclusive practices within organizations. Additionally, analytics for recruitment and retention strategies are enhanced through visual insights, contributing to more effective talent management.

Data Visualization in Big Data

In the contemporary landscape of information management, the synergy between data visualization and big data has become increasingly crucial for organizations seeking actionable insights from vast and complex datasets. Data visualization, through graphical representation techniques such as charts, graphs, and heatmaps, plays a pivotal role in distilling intricate patterns and trends inherent in massive datasets.

• It acts as a transformative bridge between raw data and meaningful insights, enabling stakeholders to comprehend complex relationships and make informed decisions.
• In tandem, big data, characterized by the exponential growth and diversity of information, provides the substantive foundation for these visualizations.

As organizations grapple with the challenges and opportunities presented by the sheer volume, velocity, and variety of data, the integration of data visualization becomes an indispensable strategy for extracting value and fostering a deeper understanding of complex information. The marriage of data visualization and big data not only enhances interpretability but also empowers decision-makers to derive actionable intelligence from the vast reservoirs of information available in today’s data-driven landscape.

Data visualization serves as a cornerstone in the modern landscape of information interpretation. Its ability to transform complex data into comprehensible visual formats, such as charts and graphs, is instrumental in facilitating better decision-making processes across various sectors.

Visualization of Data -FAQs

What are the 4 main visualization types.

Various forms of data visualization exist, including but not limited to bar charts, line charts, scatter plots, pie charts, and heat maps. These represent commonly used methods for presenting and interpreting data.

What is an example of data visualization?

A pie chart depicting the market share of different smartphone brands in a region, allowing a quick visual comparison of their respective contributions to the overall market.

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What is Visual Representation?

Home >> Neurodiversopedia >> V Terms

Visual representation means using pictures, symbols, or other images to show ideas or things. It helps kids understand things better by showing them instead of just telling them.

Table of Contents

• Frequently Asked Questions
• Science Person Definition

Real World Example of Visual Representation

How does visual representation work, recommended products, related topics, frequently asked question.

Can visual representation help with communication difficulties?

Absolutely! Visual communication tools, like icons and symbols, offer an alternative way for children to express themselves, bridging communication gaps and promoting interaction.

What types of visual tools are commonly used for kids with special needs?

Common visual tools include symbols, pictures, charts, graphs, interactive apps, and social stories that aid comprehension, communication, and learning.

Can visual representation help improve organization and daily routines?

Absolutely, visual schedules and color-coded systems help kids follow routines, stay organized, and anticipate activities, reducing anxiety and promoting independence.

Is visual representation only for young children or can older kids also benefit?

Visual representation is beneficial for children of all ages, including older kids and teenagers. It helps convey complex information, foster independence, and enhance comprehension across various age groups.

Scientific Definition

Visual representation is conveying information, concepts, or data through visual means such as images, charts, graphs, and diagrams. It is crucial in facilitating comprehension and communication, especially for children with special needs . Visual elements make information more accessible and understandable, promoting effective learning and engagement. Unlike solely relying on words, visual representation taps into the brain’s natural ability to process and retain visual information, making it an essential tool in the educational journey of children with diverse learning needs.

Helpful Resources

• Autism Society

Video Explanation

Here’s a look at how visual representation can help kids with special needs. Meet Max, a 7-year-old with ADHD . Max often finds it tough to follow classroom routines. To help him, his teacher uses visual schedules :

• Pictures of daily activities are put on a board.
• Step-by-step images show Max how to start his morning routine.
• Visual cues help Max remember what to do next.

With this system, Max feels more organized and less anxious about what comes next in his day.

Sponsored by - Goally

Visual representation helps make abstract ideas clearer for kids with special needs. Here’s how it’s used:

• Visual schedules display daily routines with pictures.
• Charts and graphs show progress or steps in a task.
• Pictograms break down instructions into easy-to-understand images.
• Social stories use visuals to teach appropriate behaviors.

These tools make learning and following instructions simpler and more engaging.

This post was originally published on 08/26/2023. It was updated on 08/07/2024. 

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What is an Infographic? (Examples, Tips and Templates)

Written by: Mahnoor Sheikh

An infographic is a powerful way to communicate data, ideas and knowledge in a visual format. It combines visual elements like charts, diagrams and illustrations, along with minimal text to explain topics in a way that’s easy to grasp.

While it may seem like a new concept, infographics have been around for a long time

In fact, they’ve exploded in popularity in virtually every industry. From digital marketing to schools and classrooms, infographics are being used everywhere to communicate complex information in a visually engaging way.

If you’re new to design and don't know anything about infographics or how to make the most of it, this short guide is created just for you.

In this article, we’ve covered everything you need to know about creating infographics. You’ll also find creative infographic examples and editable templates sprinkled throughout this article so you can start creating your own visuals .

Here’s a short selection of 10 easy-to-edit infographic templates you can edit, share and download with Visme. View more templates below:

Table of Contents

• What Is an Infographic?

Why Should You Use Infographics?

How to create an infographic, types of infographics and when to use them.

• Tips To Make Your Infographic Stand Out
• An infographic is a visual representation of data, such as a chart, graph, or image, accompanied by minimal text. It is designed to provide a clear and easily understood summary of a complex topic.
• Marketers can use infographics to increase website traffic, boost visibility and brand awareness, and lift engagement. Educators and trainers can use infographics to simplify complex information and make it more understandable.
• To create infographics in Visme, choose a template, customize it with your content and design elements, incorporate animation and interactive elements, download the infographics in multiple formats, or share them online with a URL.
• Follow these tips to make your infographics stand out: know your audience, be original and creative, use appealing and fonts, illustrate text with icons and graphics, establish visual hierarchy and make your infographics interactive.
• Visme comes packed with thousands of professionally designed templates, millions of design assets, dozens of intuitive features, AI-powered tools, collaboration and workflow management features to streamline your infographic creation process. Try out Visme's infographic maker for free and take it for a test drive!

What is an Infographic?

By definition, an infographic is a visual representation of any kind of information, data or knowledge. Infographics combine various elements, such as text, images, charts, and diagrams, to provide a clear, engaging and easily understood summary of a complex topic.

Whether it’s a study on market trends or a step-by-step guide on how to do your laundry, an infographic can help you present that information in the form of an attractive visual graphic.

Take a look at the infographic example below from the Visme template library.

It explains how marketers can use content as a selling tool. Notice how the use of bright colors, illustrated icons, and bold text instantly grabs your attention and gives you an overview of the topic as you skim through it.

Keep in mind that the goal of an infographic is not only to inform but also to make the viewing experience fun and engaging for your audience. As you can see in the infographic below, there are different types of infographics you can create.

The secret to creating an exceptional infographic all comes down to how you combine different graphic elements—like colors, icons, images, illustrations and fonts—to explain a topic in a compelling and easy-to-understand way.

Here’s another infographic above that showcases the statistics and percentages in a visual form using a combination of pie charts , bar graphs , icons and maps . So, even if you don’t read the text above the data widgets, you’ll still get the picture.

There’s a reason why infographics are so popular—they’re fun, engaging and super easy to share. Plus, they have tons of benefits for all kinds of content creators, including businesses, educators and nonprofits.

Marketers can use infographics to drive more website traffic, increase visibility and brand awareness, and boost engagement. They are also highly linkable content assets, meaning they can generate valuable backlinks that boost SEO efforts.

Educators and trainers can use infographics to explain difficult concepts or break down complex information to make it easier to understand.

Nonprofits can use infographics to spread awareness about a cause or social issue.

Here’s an example of how the Environmental Protection Agency (EPA) is using an infographic to create awareness about leaks.

Image Source

In this article, we’ve shared 101 infographic examples to give you all the inspiration you need to create beautiful infographics.

Creating a beautiful infographic can be tricky, especially if you’re not a professional designer. However, Visme makes it a breeze.

With a Visme account, you get access to thousands of professionally designed infographic templates , millions of design assets and dozens of advanced features.

You don’t need to be a designer to use it, but even your designer would love it!

Check out this video to find out how you can create an infographic in Visme.

Additionally, here’s a step-by-step guide to creating an infographic using Visme’s drag-and-drop editor and premade templates.

Step 1: Choose a Template

The first step is to sign up on Visme (it's free!) and choose a template to get started with.

Browse through hundreds of free and premium infographic templates inside the dashboard to find one that works best with your content and purpose.

When you find one you like, hover on it and click “Edit.”

Customize these infographic templates using a drag-and-drop editor! Edit and Download

If you need help or are running against the clock, Visme’s AI Designer can do the heavy lifting for you.

Just write a detailed prompt describing the type of infographic you want to create, along with the details of the design and content. Choose your design theme and watch the tool generate compelling infographics filled with text, charts and other design elements.

Step 2: Customize With Your Own Content

Whether you're using the template or the AI designer, Visme gives you complete control over your infographic design and content.

When you finally select a template or design theme to edit, you’ll redirected to the Visme editor.

This is where you get to customize the infographic with your own colors, fonts, text, images, icons and much more and make it entirely your own.

Start by replacing the dummy text with yours. Even if you’re facing writer's block, Visme AI text writer is right there to help you. With a text prompt, you can generate content ideas for your infographics and create outlines, headlines and draft copy. It can also assist in proofreading and editing your text for grammar, clarity, and conciseness.

In fact, you can change the entire color scheme of your infographic in one go using our preset color themes. Or automatically generate with the Brand Design Tool and save your assets in your brand kit. This allows you to apply your branding to any design with a single click.

Visme also gives you unlimited access to the following:

• Millions of stunning design assets, including 2D and 3D characters, icons, illustrations, shapes and graphics
• Extensive library of premium stock photos

Even if you don't find visuals that align with your vision, Visme offers an AI image generator . Whip up beautiful visuals in a variety of output styles: photos, paintings, pencil drawings, 3D graphics, icons, abstract art, and more—using a text prompt

Exciting, right? The best part is that this takes literally a few minutes because of the drag-and-drop editor.

You can also add links and animations, upload your own brand assets, add data visualizations like charts and graphs, and add new content blocks to extend your infographic.

Check out our infographic design guide for more tips on how to make an eye-catching infographic!

Step 3: Incorporate Animation and Interactive Elements

Elevate your infographic and hold the reader's attention with animation and interactive features like animated graphics and charts, animated text and objects, gestures and illustrations, links, hotspots, hover effects and pop-ups.

Bring life to your infographic by adding cool and sleek 3D animated characters. With our 3D character creator , you can customize their apparel, hair, gender, skin tone, and more until you’re satisfied with the final appearance. You also have the freedom to select their entry, waiting and exit poses.

But that's not all…You can embed interactive content in your infographic, like forms, audio, video and GIFs to make it memorable.

Step 4: Download and Share Your Infographic

That’s it—you’re almost done!

After you’ve finished customizing your infographic, it’s time to download or share it with your audience in a variety of ways.

You can either download it for offline use in image, PDF or HTML5 formats. Or generate a link to share it privately with specific people.

You can also embed it on your website or blog using a responsive code, or publish it on the web so anyone can search for and access it.

After you’ve shared your infographics online, use Visme’s analytics to keep an eye on how your audience engages with it. ​​You can find out who viewed it, the date and time of the view, the visitor's IP address, their location, the time spent on your project, and which sections they spent more time on.

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There’s no one-size-fits-all infographic out there.

Infographics are of various different types, and if you want yours to actually be effective, you need to pick a type that’s aligned with your purpose and nature of content.

In this video, we've covered the 13 types of infographics and when to use them, plus we've included templates to help you get started.

Generally, infographics are used for one or more of the following reasons:

• Illustrating data: Present statistics, facts and figures visually using charts, graphs and other graphic tools.
• Simplifying a complex subject: Explain difficult concepts with the help of illustrations and visual cues.
• Drawing a comparison: Visually compare two or more products, services, features, brands or concepts.
• Creating awareness: Spread word about an important cause or create brand awareness and visibility.
• Summarizing longer content: Repurpose long videos, blog posts and reports into bite-sized infographics.

Once you’re sure about what you need an infographic for, you can move on to selecting the right type of infographic for your needs.

Here are the different types of infographics available in Visme.

• Statistical infographics
• Informational infographics
• Process infographics
• Timeline infographics
• Anatomical infographics
• Hierarchical infographics
• List infographics
• Comparison infographics
• Location-based infographics
• Visual resume infographics

Statistical Infographics

Statistical infographics make use of typography, charts and graphs to present research, facts and figures in a visual way. This helps make data look more interesting and easier to digest than a bunch of plain numbers or tables.

A statistical infographic can either focus on a single research or data visualization or use a mix of different visualizations to present various facts and figures about a topic.

The infographic template below displays data about the global penetration of social platforms using bar graphs

This type of statistical infographic is ideal for use as part of a report or presentation or for visualizing a statistic mentioned in your blog post.

Now, take a look at the infographic template below. Instead of a single visualization, it focuses on giving a statistical overview of a more general topic—cybersecurity.

This type of statistical infographics is ideal for educational purposes and creating awareness about a subject or cause.

Statistical infographics are usually less text-heavy and more data-focused. In the Visme editor, you get access to 50+ types of data visualizations , including charts and graphs , maps , diagrams, histograms , pictograms and widgets—in 2D and 3D formats.

Informational Infographics

Informational infographics use a mix of text and visual elements to explain or simplify a topic, or guide readers through a series of steps.

The example below explains how to grow an email list with the help of a colorful informational infographic that’s easy to follow and fun to read.

These are usually text-heavy infographics and can be used to summarize long blog posts and videos. You can also share an informational infographic as a stand-alone content piece.

Check out this informational infographic template to use for your own content.

Informational infographics usually follow a visual narrative to tell a story. This includes leveraging font size and styling, imagery and the placement of different objects to lead the reader’s eye from one point to the next in the order of importance or position.

Process Infographics

Process infographics usually make use of flowcharts, diagrams and even timelines to guide readers through a series of steps or to help simplify the decision-making process.

Here’s an example of a process infographic template.

These types of infographics are useful for giving instructions to employees, explaining a step-by-step process to customers or for light-hearted, humorous purposes .

Timeline Infographics

Timeline infographics are useful for presenting information in a chronological order. Whether you’re visually showcasing your brand history or showing how something has evolved over time, a timeline infographic can help you out.

Here’s a horizontal timeline infographic template from Visme to get you started.

If you want to create a vertical timeline infographic, below is a template to help you.

You can use a timeline infographic to creatively display your brand story on your website’s About page.

This type of infographic can also help you with project management purposes, such as creating project timelines.

Anatomical Infographics

Looking to break down and explain the different parts of something? An anatomical infographic can help you do just that.

This type of infographic has a labeled diagram format, which can help you highlight and explain ingredients, product parts, characteristics, personality traits, and more.

Check out this anatomical infographic template as an example. It breaks down the key elements of a great ad copy.

Here’s another example of an anatomical infographic that explains the different moving parts of a sales business plan .

Hierarchical Infographics

This type of infographic usually features a pyramid to help you display different levels of information.

Take a look at the hierarchical infographic template below.

If you want to organize information by different levels, such as by priority, importance or difficulty, a hierarchical infographic like the one above is a good way to go.

List Infographics

List infographics help you summarize and present list-based information. This could be a list of items, factors and even steps to do something.

Here’s a list infographic template you can customize.

You can use this type of infographic to sum up a how-to or listicle blog post. List infographics are also likely to get shared as they’re usually straightforward and fun to read.

Comparison Infographics

Comparison infographics are useful for comparing multiple objects, people, concepts, products or brands. Visually comparing ideas can help illustrate similarities and differences.

Here’s an example of a comparison infographic template.

These types of infographics usually have a multi-column layout, which is useful for comparing and contrasting two different topics side by side.

Another type of comparison infographic is a comparison chart , which compares and contrasts multiple features or brands in the form of a visual table.

Here’s a comparison chart template for that purpose.

Location-Based Infographics

If you want to showcase geographic information in a visual form, a location-based or map infographic is a great option.

Here’s a map infographic template from Visme.

Map or location infographics can be used to display local, national or global data and statistics. You can color-code the map to highlight different regions, and even make them interactive by adding hover effects, links and animations.

Location-based infographics make excellent visuals to add to your blog posts, reports and presentations. You can also use them as solo graphics and share on social media to drive engagement and traffic.

Visual Resume Infographics

Employers receive hundreds of resumes at a time and only a handful of them manage to stand out from the pile. If you want to take your resume to the next level , consider creating a visual resume infographic.

Check out this creative resume infographic template.

Customize this infographic template and make it your own! Edit and Download

Resume infographics offer a refreshingly new take on the standard infographic style. They make use of visualizations like radials, progress bars, icons and arrows to illustrate skills, interests, experiences and more.

Tips to Make Your Infographic Stand Out

Now that you know the different types of infographics you can use and how to create one for yourself, here are some tips to help you take your visuals to the next level.

1. Know Your Audience

The most important piece of homework you need to do before creating an infographic is to find out if it will actually work with your audience.

Understand the kind of topics they like and what type of design will appeal to them. You also need to know the kind of tone that works with your audience, as you’ll use that to craft compelling copy for your infographic.

You also need to know the social channels that are most used by your audience so you can create an infographic that is optimized to perform best on those specific platforms.

There are plenty of ways to figure out what type of infographic your audience will connect with.

• Create buyer personas to understand the common characteristics your audience share.
• Conduct surveys to find out about their topics of interest, preferred content formats, and pain points.
• Analyze your website and social media analytics to find out what type of content performs best.
• Keep an eye on trending and hot topics in your industry and community.
• Stay in tune with conversations on social communities like Reddit, Quora, Twitter, LinkedIn and more.
• Lastly, check out the type of competitors are publishing and how their audience responds to them.

2. Be Original and Creative

There are millions of infographics and visuals floating on the internet. If you want to get yours noticed, create something unique and different.

Do some research before creating your infographic. Find out what kind of topics will appeal to your audience and what questions they might have unanswered. You can do keyword research to find topics that resonate with your audience.

Then go ahead and do a Google search to see if there are any existing interesting visuals or infographics on the topic.

If it’s a topic that’s already been covered before but you still want to create an infographic about it, make sure you create it with a fresh, new angle.

For example, if you're creating an infographic on improving employee productivity, analyze existing ones to see if they focus primarily on time management and overlook technology integration. This gap could be your unique angle.

Using original research or data can set your infographic apart. So you want to conduct surveys, collect unique statistics, interview experts or use case studies to provide fresh insights not found elsewhere.

3. Use Appealing Colors and Fonts

Color psychology is real. And marketers all over the world rely on it to create effective designs that actually bring results.

If your infographic doesn’t use colors and fonts that resonate with your audience or bring your content to life, it might fail to stand out.  In Visme, you get dozens of pre-made color combinations or create and save your own color palettes for future design projects.

Remember to keep your infographic consistent with your brand identity. This creates a cohesive look and helps with brand recall.

Visme’s brand wizard can help pull in all of your brand assets and save them in your brand kit. All you need to do is input your URL. You’ll even get beautiful templates crafted to suit your branding.

Check out these resources put together by experts to help you choose the best colors and fonts for your infographic designs.

• Color Psychology in Marketing: The Ultimate Guide
• A Non-Designer's Guide to Pairing Fonts

Additionally, check out this video on color psychology in marketing to help guide your designs.

4. Illustrate Text with Icons and Graphics.

Too much text can make an infographic look boring and uninteresting.

Make sure you use as many visuals and as less text as you can. One way to do that is to replace or supplement subheadings, labels, captions and other text in your infographic with icons, illustrations or images.

Don’t settle for run-of-the-mill visuals that lack aesthetic value. Instead, invest in high-quality images, icons, illustrations, and graphics that captivate your audience and hold their attention. As we mentioned earlier, Visme comes packed with millions of stock photos and other types of design assets to make your infographics pack a punch. You can even build unique characters that take your infographic storytelling to the next level.

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5. Establish a Visual Hierarchy.

Visual hierarchy is all about organizing information on your design according to the level of importance or order so that the reader’s eye naturally goes from one section to the next.

Establishing a visual hierarchy makes your infographic design look cleaner, more attractive and more professional instead of cluttered with all kinds of information.

For example, headings, subheadings, and sections can help break up the information into sections. Different font sizes and weights can help show which parts are more important, while bold or larger fonts can make key statistics or important points stand out.

Another critical element of visual hierarchy is spacing and alignment. With ample white space, your infographic looks uncluttered, clean, organized and easy to read.

This infographic shares a brief overview of the six elements of visual hierarchy.

6. Make it interactive.

If you really want to make your infographic stand out, consider taking it a step further from just being a static image.

The reason is simple—-attention spans are shrinking. Adding an element of interactivity will do a great job of captivating and holding your audience’s attention.

Visme offers a wide array of animation and interactive features that will not only elevate your infographics but also engage viewers and enhance the overall experience.

You can add animated graphics, charts, text, objects, gestures and illustrations to your infographic, insert clickable links, hotspots and buttons, and even add pop-ups and hover effects.

For example, you can include hotspots that, when hovered over, display additional data or context and links to in-depth reports or case studies.

Supercharge your infographics by incorporating trendy 3D animated characters and widgets for a more dynamic visual experience.

What’s more…Embed interactive content like forms, audio, video, and GIFs to make your infographic memorable and engaging. These elements can provide deeper insights, encourage viewer participation, and make the infographic more dynamic.

Start Creating Your Own Infographics

Infographics are one of the most effective types of content out there, for good reason.

They’re visual, shareable, fun to look at, and can make even the most boring and technical information look interesting.

Now that you know what an infographic is, when to use it and how to create one, it’s time to get started with your own. Visme offers thousands of professionally designed templates, millions of design assets, dozens of intuitive features, AI-powered tools, collaboration and workflow management features to help streamline your content creation process.

Try out Visme's infographic maker for free and take it for a test drive!

Happy creating!

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About the Author

Mahnoor Sheikh is the content marketing manager at Visme. She has years of experience in content strategy and execution, SEO copywriting and graphic design. She is also the founder of MASH Content and is passionate about tea, kittens and traveling with her husband. Get in touch with her on LinkedIn .

How to develop a graphical framework to chart your research

Graphic representations or frameworks can be powerful tools to explain research processes and outcomes. David Waller explains how researchers can develop effective visual models to chart their work

David Waller

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While undertaking a study, researchers can uncover insights, connections and findings that are extremely valuable to anyone likely to read their eventual paper. Thus, it is important for the researcher to clearly present and explain the ideas and potential relationships. One important way of presenting findings and relationships is by developing a graphical conceptual framework.

A graphical conceptual framework is a visual model that assists readers by illustrating how concepts, constructs, themes or processes work. It is an image designed to help the viewer understand how various factors interrelate and affect outcomes, such as a chart, graph or map.

These are commonly used in research to show outcomes but also to create, develop, test, support and criticise various ideas and models. The use of a conceptual framework can vary depending on whether it is being used for qualitative or quantitative research.

• Using literature reviews to strengthen research: tips for PhDs and supervisors
• Get your research out there: 7 strategies for high-impact science communication
• Understanding peer review: what it is, how it works and why it is important

There are many forms that a graphical conceptual framework can take, which can depend on the topic, the type of research or findings, and what can best present the story.

Below are examples of frameworks based on qualitative and quantitative research.

As shown by the table below, in qualitative research the conceptual framework is developed at the end of the study to illustrate the factors or issues presented in the qualitative data. It is designed to assist in theory building and the visual understanding of the exploratory findings. It can also be used to develop a framework in preparation for testing the proposition using quantitative research.

In quantitative research a conceptual framework can be used to synthesise the literature and theoretical concepts at the beginning of the study to present a model that will be tested in the statistical analysis of the research.

It is important to understand that the role of a conceptual framework differs depending on the type of research that is being undertaken.

So how should you go about creating a conceptual framework? After undertaking some studies where I have developed conceptual frameworks, here is a simple model based on “Six Rs”: Review, Reflect, Relationships, Reflect, Review, and Repeat.

Process for developing conceptual frameworks:

Review: literature/themes/theory.

Reflect: what are the main concepts/issues?

Relationships: what are their relationships?

Reflect: does the diagram represent it sufficiently?

Review: check it with theory, colleagues, stakeholders, etc.

Repeat: review and revise it to see if something better occurs.

This is not an easy process. It is important to begin by reviewing what has been presented in previous studies in the literature or in practice. This provides a solid background to the proposed model as it can show how it relates to accepted theoretical concepts or practical examples, and helps make sure that it is grounded in logical sense.

It can start with pen and paper, but after reviewing you should reflect to consider if the proposed framework takes into account the main concepts and issues, and the potential relationships that have been presented on the topic in previous works.

It may take a few versions before you are happy with the final framework, so it is worth continuing to reflect on the model and review its worth by reassessing it to determine if the model is consistent with the literature and theories. It can also be useful to discuss the idea with  colleagues or to present preliminary ideas at a conference or workshop –  be open to changes.

Even after you come up with a potential model it is good to repeat the process to review the framework and be prepared to revise it as this can help in refining the model. Over time you may develop a number of models with each one superseding the previous one.

A concern is that some students hold on to the framework they first thought of and worry that developing or changing it will be seen as a weakness in their research. However, a revised and refined model can be an important factor in justifying the value of the research.

Plenty of possibilities and theoretical topics could be considered to enhance the model. Whether it ultimately supports the theoretical constructs of the research will be dependent on what occurs when it is tested.  As social psychologist, Kurt Lewin, famously said “ There's nothing so practical as good theory ”.

The final result after doing your reviewing and reflecting should be a clear graphical presentation that will help the reader understand what the research is about as well as where it is heading.

It doesn’t need to be complex. A simple diagram or table can clarify the nature of a process and help in its analysis, which can be important for the researcher when communicating to their audience. As the saying goes: “ A picture is worth 1000 words ”. The same goes for a good conceptual framework, when explaining a research process or findings.

David Waller is an associate professor at the University of Technology Sydney .

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5. Visual Representation

How can you design computer displays that are as meaningful as possible to human viewers? Answering this question requires understanding of visual representation - the principles by which markings on a surface are made and interpreted. The analysis in this article addresses the most important principles of visual representation for screen design, introduced with examples from the early history of graphical user interfaces . In most cases, these principles have been developed and elaborated within whole fields of study and professional skill - typography , cartography, engineering and architectural draughting, art criticism and semiotics. Improving on the current conventions requires serious skill and understanding. Nevertheless, interaction designers should be able, when necessary, to invent new visual representations.

Introduction to Visual Representation by Alan Blackwell

Alan Blackwell on applying theories of Visual Representation

• 5.1 Typography and text

For many years, computer displays resembled paper documents. This does not mean that they were simplistic or unreasonably constrained. On the contrary, most aspects of modern industrial society have been successfully achieved using the representational conventions of paper, so those conventions seem to be powerful ones. Information on paper can be structured using tabulated columns, alignment, indentation and emphasis, borders and shading. All of those were incorporated into computer text displays. Interaction conventions, however, were restricted to operations of the typewriter rather than the pencil. Each character typed would appear at a specific location. Locations could be constrained, like filling boxes on a paper form. And shortcut command keys could be defined using onscreen labels or paper overlays. It is not text itself, but keyboard interaction with text that is limited and frustrating compared to what we can do with paper (Sellen and Harper 2001).

But despite the constraints on keyboard interaction, most information on computer screens is still represented as text. Conventions of typography and graphic design help us to interpret that text as if it were on a page, and human readers benefit from many centuries of refinement in text document design. Text itself, including many writing systems as well as specialised notations such as algebra, is a visual representation that has its own research and educational literature. Documents that contain a mix of bordered or coloured regions containing pictures, text and diagrammatic elements can be interpreted according to the conventions of magazine design, poster advertising, form design, textbooks and encyclopaedias. Designers of screen representations should take care to properly apply the specialist knowledge of those graphic and typographic professions. Position on the page, use of typographic grids, and genre-specific illustrative conventions should all be taken into account.

Author/Copyright holder: Unknown (pending investigation). Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.1 : Contemporary example from the grid system website

Figure 5.2 : Example of a symbolic algebra expression (the single particle solution to Schrodinger's equation)

Figure 5.3 : Table layout of funerals from the plague in London in 1665

Figure 5.4 : Tabular layout of the first page of the Gutenberg Bible: Volume 1, Old Testament, Epistle of St. Jerome. The Gutenberg Bible was printed by Johannes Gutenberg, in Mainz, Germany in the 1450s

• 5.1.1 Summary

Most screen-based information is interpreted according to textual and typographic conventions, in which graphical elements are arranged within a visual grid, occasionally divided or contained with ruled and coloured borders. Where to learn more:

thegridsystem.org

Resnick , Elizabeth (2003): Design for Communication: Conceptual Graphic Design Basics. Wiley

• 5.2 Maps and graphs

The computer has, however, also acquired a specialised visual vocabulary and conventions. Before the text-based computer terminal (or 'glass teletype') became ubiquitous, cathode ray tube displays were already used to display oscilloscope waves and radar echoes. Both could be easily interpreted because of their correspondence to existing paper conventions. An oscilloscope uses a horizontal time axis to trace variation of a quantity over time, as pioneered by William Playfair in his 1786 charts of the British economy. A radar screen shows direction and distance of objects from a central reference point, just as the Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem. Many visual displays on computers continue to use these ancient but powerful inventions - the map and the graph. In particular, the first truly large software project, the SAGE air defense system, set out to present data in the form of an augmented radar screen - an abstract map, on which symbols and text could be overlaid. The first graphics computer, the Lincoln Laboratory Whirlwind, was created to show maps, not text.

Figure 5.5 : The technique invented by William Playfair, for visual representation of time series data.

Author/Copyright holder: Courtesy of Premek. V. Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.6 : Time series data as shown on an oscilloscope screen

Author/Copyright holder: Courtesy of Remi Kaupp. Copyright terms and licence: CC-Att-SA (Creative Commons Attribution-ShareAlike 3.0 Unported)

Figure 5.7 : Early radar screen from HMS Belfast built in 1936

Author/Copyright holder: Courtesy of NOAA's National Weather Service. Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.8 : Early weather radar - Hurricane Abby approaching the coast of British Honduras in 1960

Figure 5.9 : The Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem

Author/Copyright holder: Courtesy of Wikipedia. Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.10 : The SAGE system in use. The SAGE system used light guns as interaction devices.

Author/Copyright holder: The MITRE Corporation. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.11 : The Whirlwind computer at the MIT Lincoln Laboratory

• 5.2.1 Summary

Basic diagrammatic conventions rely on quantitative correspondence between a direction on the surface and a continuous quantity such as time or distance. These should follow established conventions of maps and graphs.

Where to learn more:

MacEachren , Alan M. (2004): How Maps Work: Representation, Visualization, and Design. The Guilford Press

• 5.3 Schematic drawings

Ivan Sutherland's groundbreaking PhD research with Whirlwind's successor TX-2 introduced several more sophisticated alternatives (Sutherland 1963). The use of a light pen allowed users to draw arbitrary lines, rather than relying on control keys to select predefined options. An obvious application, in the engineering context of Massachusetts Institute of Technology (MIT) where Sutherland worked, was to make engineering drawings such as the girder bridge in Figure 13. Lines on the screen are scaled versions of the actual girders, and text information can be overlaid to give details of force calculations. Plans of this kind, as a visual representation, are closely related to maps. However, where the plane of a map corresponds to a continuous surface, engineering drawings need not be continuous. Each set of connected components must share the same scale, but white space indicates an interpretive break, so that independent representations can potentially share the same divided surface - a convention introduced in Diderot's encyclopedia of 1772, which showed pictures of multiple objects on a page, but cut them loose from any shared pictorial context.

Author/Copyright holder: Courtesy of Ivan Sutherland. Copyright terms and licence: CC-Att-SA-3 (Creative Commons Attribution-ShareAlike 3.0).

Figure 5.12 : The TX-2 graphics computer, running Ivan Sutherland's Sketchpad software

Figure 5.13 : An example of a force diagram created using Sutherland's Sketchpad

Figure 5.14 : A page from the Encyclopédie of Diderot and d'Alembert, combining pictorial elements with diagrammatic lines and categorical use of white space.

• 5.3.1 Summary

Engineering drawing conventions allow schematic views of connected components to be shown in relative scale, and with text annotations labelling the parts. White space in the representation plane can be used to help the reader distinguish elements from each other rather than directly representing physical space. Where to learn more:

Engineering draughting textbooks

Ferguson , Eugene S. (1994): Engineering and the Mind's Eye. MIT Press

• 5.4 Pictures

The examples so far may seem rather abstract. Isn't the most 'natural' visual representation simply a picture of the thing you are trying to represent? In that case, what is so hard about design? Just point a camera, and take the picture. It seems like pictures are natural and intuitive, and anyone should be able to understand what they mean. Of course, you might want the picture to be more or less artistic, but that isn't a technical concern, is it? Well, Ivan Sutherland also suggested the potential value that computer screens might offer as artistic tools. His Sketchpad system was used to create a simple animated cartoon of a winking girl. We can use this example to ask whether pictures are necessarily 'natural', and what design factors are relevant to the selection or creation of pictures in an interaction design context.

We would not describe Sutherland's girl as 'realistic', but it is an effective representation of a girl. In fact, it is an unusually good representation of a winking girl, because all the other elements of the picture are completely abstract and generic. It uses a conventional graphic vocabulary of lines and shapes that are understood in our culture to represent eyes, mouths and so on - these elements do not draw attention to themselves, and therefore highlight the winking eye. If a realistic picture of an actual person was used instead, other aspects of the image (the particular person) might distract the viewer from this message.

Figure 5.15 : Sutherland's 'Winking Girl' drawing, created with the Sketchpad system

It is important, when considering the design options for pictures, to avoid the 'resemblance fallacy', i.e. that drawings are able to depict real object or scenes because the viewer's perception of the flat image simulates the visual perception of a real scene. In practice, all pictures rely on conventions of visual representation, and are relatively poor simulations of natural engagement with physical objects, scenes and people. We are in the habit of speaking approvingly of some pictures as more 'realistic' than others (photographs, photorealistic ray-traced renderings, 'old master' oil paintings), but this simply means that they follow more rigorously a particular set of conventions. The informed designer is aware of a wide range of pictorial conventions and options.

As an example of different pictorial conventions, consider the ways that scenes can be rendered using different forms of artistic perspective. The invention of linear perspective introduced a particular convention in which the viewer is encouraged to think of the scene as perceived through a lens or frame while holding his head still, so that nearby objects occupy a disproportionate amount of the visual field. Previously, pictorial representations more often varied the relative size of objects according to their importance - a kind of 'semantic' perspective. Modern viewers tend to think of the perspective of a camera lens as being most natural, due to the ubiquity of photography, but we still understand and respect alternative perspectives, such as the isometric perspective of the pixel art group eBoy, which has been highly influential on video game style.

Author/Copyright holder: Courtesy of Masaccio (1401-1428). Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship))

Figure 5.16 : Example of an early work by Masaccio, demonstrating a 'perspective' in which relative size shows symbolic importance

Author/Copyright holder: eBoy.com. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.17 : Example of the strict isometric perspective used by the eBoy group

Author/Copyright holder: Courtesy of Masaccio (1401-1428). Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.18 : Masaccio's mature work The Tribute Money, demonstrating linear perspective

As with most conventions of pictorial representation, new perspective rendering conventions are invented and esteemed for their accuracy by critical consensus, and only more slowly adopted by untrained readers. The consensus on preferred perspective shifts across cultures and historical periods. It would be naïve to assume that the conventions of today are the final and perfect product of technical evolution. As with text, we become so accustomed to interpreting these representations that we are blind to the artifice. But professional artists are fully aware of the conventions they use, even where they might have mechanical elements - the way that a photograph is framed changes its meaning, and a skilled pencil drawing is completely unlike visual edge-detection thresholds. A good pictorial representation need not simulate visual experience any more than a good painting of a unicorn need resemble an actual unicorn. When designing user interfaces, all of these techniques are available for use, and new styles of pictorial rendering are constantly being introduced.

• 5.4.1 Summary

Pictorial representations, including line drawings, paintings, perspective renderings and photographs rely on shared interpretive conventions for their meaning. It is naïve to treat screen representations as though they were simulations of experience in the physical world. Where to learn more:

Micklewright , Keith (2005): Drawing: Mastering the Language of Visual Expression. Harry N. Abrams

Stroebel , Leslie, Todd , Hollis and Zakia , Richard (1979): Visual Concepts for Photographers. Focal Press

• 5.5 Node-and-link diagrams

The first impulse of a computer scientist, when given a pencil, seems to be to draw boxes and connect them with lines. These node and link diagrams can be analysed in terms of the graph structures that are fundamental to the study of algorithms (but unrelated to the visual representations known as graphs or charts). A predecessor of these connectivity diagrams can be found in electrical circuit schematics, where the exact location of components, and the lengths of the wires, can be arranged anywhere, because they are irrelevant to the circuit function. Another early program created for the TX-2, this time by Ivan Sutherland's brother Bert, allowed users to create circuit diagrams of this kind. The distinctive feature of a node-and-link connectivity diagram is that, since the position of each node is irrelevant to the operation of the circuit, it can be used to carry other information. Marian Petre's research into the work of electronics engineers (Petre 1995) catalogued the ways in which they positioned components in ways that were meaningful to human readers, but not to the computer - like the blank space between Diderot's objects this is a form of 'secondary notation' - use of the plane to assist the reader in ways not related to the technical content.

Circuit connectivity diagrams have been most widely popularised through the London Underground diagram, an invention of electrical engineer Henry Beck. The diagram clarified earlier maps by exploiting the fact that most underground travellers are only interested in order and connectivity, not location, of the stations on the line. (Sadly, the widespread belief that a 'diagram' will be technical and hard to understand means that most people describe this as the London Undergound 'map', despite Beck's insistence on his original term).

Author/Copyright holder: Courtesy of Harry C. Beck and possibly F. H. Stingemore, born 1890, died 1954. Stingmore designed posters for the Underground Group and London Transport 1914-1942. Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.19 : Henry Beck's London Underground Diagram (1933)

Author/Copyright holder: Computer History Museum, Mountain View, CA, USA. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.20 : Node and link diagram of the kind often drawn by computing professionals

Figure 5.21 : Map of the London Underground network, as it was printed before the design of Beck's diagram (1932)

• 5.5.1 Summary

Node and link diagrams are still widely perceived as being too technical for broad acceptance. Nevertheless, they can present information about ordering and relationships clearly, especially if consideration is given to the value of allowing human users to specify positions. Where to learn more:

Diagrammatic representation books

Lowe , Ric (1992): Successful Instructional Diagram.

• 5.6 Icons and symbols

Maps frequently use symbols to indicate specific kinds of landmark. Sometimes these are recognisably pictorial (the standard symbols for tree and church), but others are fairly arbitrary conventions (the symbol for a railway station). As the resolution of computer displays increased in the 1970s, a greater variety of symbols could be differentiated, by making them more detailed, as in the MIT SDMS (Spatial Data Management System) that mapped a naval battle scenario with symbols for different kinds of ship. However, the dividing line between pictures and symbols is ambiguous. Children's drawings of houses often use conventional symbols (door, four windows, triangle roof and chimney) whether or not their own house has two storeys, or a fireplace. Letters of the Latin alphabet are shapes with completely arbitrary relationship to their phonetic meaning, but the Korean phonetic alphabet is easier to learn because the forms mimic the shape of the mouth when pronouncing those sounds. The field of semiotics offers sophisticated ways of analysing the basis on which marks correspond to meanings. In most cases, the best approach for an interaction designer is simply to adopt familiar conventions. When these do not exist, the design task is more challenging.

It is unclear which of the designers working on the Xerox Star coined the term 'icon' for the small pictures symbolising different kinds of system object. David Canfield Smith winningly described them as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts. But 'icon' is also used as a technical term in semiotics. Unfortunately, few of the Xerox team had a sophisticated understanding of semiotics. It was fine art PhD Susan Kare's design work on the Apple Macintosh that established a visual vocabulary which has informed the genre ever since. Some general advice principles are offered by authors such as Horton (1994), but the successful design of icons is still sporadic. Many software publishers simply opt for a memorable brand logo, while others seriously misjudge the kinds of correspondence that are appropriate (my favourite blooper was a software engineering tool in which a pile of coins was used to access the 'change' command).

It has been suggested that icons, being pictorial, are easier to understand than text, and that pre-literate children, or speakers of different languages, might thereby be able to use computers without being able to read. In practice, most icons simply add decoration to text labels, and those that are intended to be self-explanatory must be supported with textual tooltips. The early Macintosh icons, despite their elegance, were surprisingly open to misinterpretation. One PhD graduate of my acquaintance believed that the Macintosh folder symbol was a briefcase (the folder tag looked like a handle), which allowed her to carry her files from place to place when placed inside it. Although mistaken, this belief never caused her any trouble - any correspondence can work, so long as it is applied consistently.

Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.22 : In art, the term Icon (from Greek, eikon, "image") commonly refers to religious paintings in Eastern Orthodox, Oriental Orthodox, and Eastern-rite Catholic jurisdictions. Here a 6th-century encaustic icon from Saint Catherine's Monastery, Mount Sinai

Author/Copyright holder: Apple Computer, Inc. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.23 : In computing, David Canfield Smith described computer icons as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts.

• 5.6.1 Summary

The design of simple and memorable visual symbols is a sophisticated graphic design skill. Following established conventions is the easiest option, but new symbols must be designed with an awareness of what sort of correspondence is intended - pictorial, symbolic, metonymic (e.g. a key to represent locking), bizarrely mnemonic, but probably not monolingual puns. Where to learn more:

Napoles , Veronica (1987): Corporate Identity Design.

• 5.7 Visual metaphor

The ambitious graphic designs of the Xerox Star/Alto and Apple Lisa/Macintosh were the first mass-market visual interfaces. They were marketed to office professionals, making the 'cover story' that they resembled an office desktop a convenient explanatory device. Of course, as was frequently noted at the time, these interfaces behaved nothing like a real desktop. The mnemonic symbol for file deletion (a wastebasket) was ridiculous if interpreted as an object placed on a desk. And nobody could explain why the desk had windows in it (the name was derived from the 'clipping window' of the graphics architecture used to implement them - it was at some later point that they began to be explained as resembling sheets of paper on a desk). There were immediate complaints from luminaries such as Alan Kay and Ted Nelson that strict analogical correspondence to physical objects would become obstructive rather than instructive. Nevertheless, for many years the marketing story behind the desktop metaphor was taken seriously, despite the fact that all attempts to improve the Macintosh design with more elaborate visual analogies , as in General Magic and Microsoft Bob, subsequently failed.

The 'desktop' can be far more profitably analysed (and extended) by understanding the representational conventions that it uses. The size and position of icons and windows on the desktop has no meaning, they are not connected, and there is no visual perspective, so it is neither a map, graph nor picture. The real value is the extent to which it allows secondary notation, with the user creating her own meaning by arranging items as she wishes. Window borders separate areas of the screen into different pictorial, text or symbolic contexts as in the typographic page design of a textbook or magazine. Icons use a large variety of conventions to indicate symbolic correspondence to software operations and/or company brands, but they are only occasionally or incidentally organised into more complex semiotic structures.

Author/Copyright holder:Apple Computer, Inc and Computer History Museum, Mountain View, CA. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.24 : Apple marketed the visual metaphor in 1983 as a key benefit of the Lisa computer. This advertisement said 'You can work with Lisa the same familiar way you work at your desk'. However a controlled study by Carroll and Mazur (1986) found that the claim for immediately familiar operation may have been exaggerated.

Figure 5.25 : The Xerox Alto and Apple Lisa, early products in which bitmapped displays allowed pictorial icons to be used as mnemonic cues within the 'desktop metaphor'

Author/Copyright holder: Courtesy of Mschlindwein. Copyright terms and licence: CC-Att-SA (Creative Commons Attribution-ShareAlike 3.0 Unported).

Figure 5.26 : Apple Lisa

• 5.7.1 Summary

Theories of visual representation, rather than theories of visual metaphor, are the best approach to explaining the conventional Macintosh/Windows 'desktop'. There is huge room for improvement. Where to learn more:

Blackwell , Alan (2006): The reification of metaphor as a design tool . In ACM Transactions on Computer-Human Interaction , 13 (4) pp. 490-530

• 5.8 Unified theories of visual representation

The analysis in this article has addressed the most important principles of visual representation for screen design, introduced with examples from the early history of graphical user interfaces. In most cases, these principles have been developed and elaborated within whole fields of study and professional skill - typography, cartography, engineering and architectural draughting, art criticism and semiotics. Improving on the current conventions requires serious skill and understanding. Nevertheless, interaction designers should be able, when necessary, to invent new visual representations.

One approach is to take a holistic perspective on visual language, information design, notations, or diagrams. Specialist research communities in these fields address many relevant factors from low-level visual perception to critique of visual culture. Across all of them, it can be necessary to ignore (or not be distracted by) technical and marketing claims, and to remember that all visual representations simply comprise marks on a surface that are intended to correspond to things understood by the reader. The two dimensions of the surface can be made to correspond to physical space (in a map), to dimensions of an object, to a pictorial perspective, or to continuous abstract scales (time or quantity). The surface can also be partitioned into regions that should be interpreted differently. Within any region, elements can be aligned, grouped, connected or contained in order to express their relationships. In each case, the correspondence between that arrangement, and the intended interpretation, must be understood by convention, explained, or derived from the structural and perceptual properties of marks on the plane. Finally, any individual element might be assigned meaning according to many different semiotic principles of correspondence.

The following table summarises holistic views, as introduced above, drawing principally on the work of Bertin, Richards, MacEachren, Blackwell & Engelhardt and Engelhardt. Where to learn more:

Engelhardt , Yuri (2002). The Language of Graphics. A framework for the analysis of syntax and meaning in maps, charts and diagrams (PhD Thesis) . University of Amsterdam

Table 5.1 : Summary of the ways in which graphical representations can be applied in design, via different systems of correspondence

Table 5.2 : Screenshot from the site gapminder.org, illustrating a variety of correspondence conventions used in different parts of the page

As an example of how one might analyse (or working backwards, design) a complex visual representation, consider the case of musical scores. These consist of marks on a paper surface, bound into a multi-page book, that is placed on a stand at arms length in front of a performer. Each page is vertically divided into a number of regions, visually separated by white space and grid alignment cues. The regions are ordered, with that at the top of the page coming first. Each region contains two quantitative axes, with the horizontal axis representing time duration, and the vertical axis pitch. The vertical axis is segmented by lines to categorise pitch class . Symbols placed at a given x-y location indicate a specific pitched sound to be initiated at a specific time. A conventional symbol set indicates the duration of the sound. None of the elements use any variation in colour, saturation or texture. A wide variety of text labels and annotation symbols are used to elaborate these basic elements. Music can be, and is, also expressed using many other visual representations (see e.g. Duignan for a survey of representations used in digital music processing).

• 5.9 Where to learn more

The historical examples of early computer representations used in this article are mainly drawn from Sutherland (Ed. Blackwell and Rodden 2003), Garland (1994), and Blackwell (2006). Historical reviews of visual representation in other fields include Ferguson (1992), Pérez-Gómez and Pelletier (1997), McCloud (1993), Tufte (1983). Reviews of human perceptual principles can be found in Gregory (1970), Ittelson (1996), Ware (2004), Blackwell (2002). Advice on principles of interaction with visual representation is distributed throughout the HCI literature, but classics include Norman (1988), Horton (1994), Shneiderman ( Shneiderman and Plaisant 2009, Card et al 1999, Bederson and Shneiderman 2003) and Spence (2001). Green's Cognitive Dimensions of Notations framework has for many years provided a systematic classification of the design parameters in interactive visual representations. A brief introduction is provided in Blackwell and Green (2003).

Research on visual representation topics is regularly presented at the Diagrams conference series (which has a particular emphasis on cognitive science ), the InfoDesign and Vision Plus conferences (which emphasise graphic and typographic information design), the Visual Languages and Human-Centric Computing symposia (emphasising software tools and development), and the InfoVis and Information Visualisation conferences (emphasising quantitative and scientific data visualisation ).

• 5.9.0.1 IV - International Conference on Information Visualization

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998

• 5.9.0.2 DIAGRAMS - International Conference on the Theory and Application of Diagrams

2008 2006 2004 2002 2000

• 5.9.0.3 VL-HCC - Symposium on Visual Languages and Human Centric Computing

2008 2007 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990

• 5.9.0.4 InfoVis - IEEE Symposium on Information Visualization

2005 2004 2003 2002 2001 2000 1999 1998 1997 1995

• 5.10 References

Anderson , Michael, Meyer , Bernd and Olivier , Patrick (2002): Diagrammatic Representation and Reasoning. London, UK,

Bederson , Benjamin B. and Shneiderman , Ben (2003): The Craft of Information Visualization : Readings and Reflections. Morgan Kaufman Publishers

Bertin , Jacques (1967): Semiology of Graphics: Diagrams, Networks, Maps (Sémiologie graphique: Les diagrammes - Les réseaux - Les cartes). English translation by W. J. Berg. Madison, WI, USA, University of Wisconsin Press

Blackwell , Alan (2002): Psychological perspectives on diagrams and their users. In: Anderson , Michael, Meyer , Bernd and Olivier , Patrick (eds.). "Diagrammatic Representation and Reasoning". London, UK: pp. 109-123

Blackwell , Alan and Engelhardt , Yuri (2002): A Meta-Taxonomy for Diagram Research. In: Anderson , Michael, Meyer , Bernd and Olivier , Patrick (eds.). "Diagrammatic Representation and Reasoning". London, UK: pp. 47-64

Blackwell , Alan and Green , T. R. G. (2003): Notational Systems - The Cognitive Dimensions of Notations Framework. In: Carroll , John M. (ed.). "HCI Models, Theories, and Frameworks". San Francisco: Morgan Kaufman Publisherspp. 103-133

Carroll , John M. and Mazur , Sandra A. (1986): LisaLearning . In Computer , 19 (11) pp. 35-49

Garland , Ken (1994): Mr. Beck's Underground Map. Capital Transport Publishing

Goodman , Nelson (1976): Languages of Art. Hackett Publishing Company

Gregory , Richard L. (1970): The Intelligent Eye. London, Weidenfeld and Nicolson

Horton , William (1994): The Icon Book: Visual Symbols for Computer Systems and Documentation. John Wiley and Sons

Ittelson , W. H. (1996): Visual perception of markings . In Psychonomic Bulletin & Review , 3 (2) pp. 171-187

Mccloud , Scott (1994): Understanding Comics: The Invisible Art. Harper Paperbacks

Norman , Donald A. (1988): The Design of Everyday Things. New York, Doubleday

Petre , Marian (1995): Why Looking Isn't Always Seeing: Readership Skills and Graphical Programming . In Communications of the ACM , 38 (6) pp. 33-44

Pérez-Gómez , Alberto and Pelletier , Louise (1997): Architectural Representation and the Perspective Hinge. MIT Press

Richards , Clive (1984). Diagrammatics: an investigation aimed at providing a theoretical framework for studying diagrams and for establishing a taxonomy of their fundamental modes of graphic organization. Unpublished Phd Thesis . Royal College of Art, London, UK

Sellen , Abigail and Harper , Richard H. R. (2001): The Myth of the Paperless Office. MIT Press

Shneiderman , Ben and Plaisant , Catherine (2009): Designing the User Interface : Strategies for Effective Human-Computer Interaction (5th ed.). Addison-Wesley

Spence , Robert (2001): Information Visualization. Addison Wesley

Sutherland , Ivan E. (1963). Sketchpad, A Man-Machine Graphical Communication System. PhD Thesis at Massachusetts Institute of Technology, online version and editors' introduction by Alan Blackwell & K. Rodden. Technical Report 574 . Cambridge University Computer Laboratory

Tufte , Edward R. (1983): The Visual Display of Quantitative Information. Cheshire, CT , Graphics Press

Ware , Colin (2004): Information Visualization: Perception for Design, 2nd Ed. San Francisco, Morgan Kaufman

• 5 Visual Representation

Human-Computer Interaction: The Foundations of UX Design

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5.10 commentary by ben shneiderman.

Since computer displays are such powerful visual appliances, careful designers devote extensive effort to getting the visual representation right. They have to balance the demands of many tasks, diverse users, and challenging requirements, such as short learning time, rapid performance, low error rates, and good retention over time. Designing esthetic interfaces that please and even delight users is a further expectation that designers must meet to be successful. For playful and discretionary tasks esthetic concerns may dominate, but for life critical tasks, rapid performance with low error rates are essential. Alan Blackwell's competent description of many visual representation issues is a great start for newcomers with helpful reminders even for experienced designers. The videos make for a pleasant personal accompaniment that bridges visual representation for interface design with thoughtful analyses of representational art. Blackwell's approach might be enriched by more discussion of visual representations in functional product design tied to meaningful tasks. Learning from paintings of Paris is fine, but aren't there other lessons to learn from visual representations in airport kiosks, automobile dashboards, or intensive care units? These devices as well as most graphical user interfaces and mobile devices raise additional questions of changing state visualization and interaction dynamics. Modern designers need to do more than show the right phone icon, they need to show ringing, busy, inactive, no network, conference mode, etc., which may include color changes (highlighted, grayed out), animations, and accompanying sounds. These designers also need to deal with interactive visual representations that happen with a click, double-click, right-click, drag, drag-and-drop, hover, multi-select, region-select, brushing-linking, and more. The world of mobile devices such as phones, cameras, music players, or medical sensors is the new frontier for design, where visual representations are dynamic and tightly integrated with sound, haptics, and novel actions such as shaking, twisting, or body movements. Even more challenging is the expectation that goes beyond the solitary viewer to the collaboration in which multiple users embedded in a changing physical environment produce new visual representations. These changing and interactive demands on designers invite creative expressions that are very different from designs for static signs, printed diagrams, or interpretive art. The adventure for visual representation designers is to create a new language of interaction that engages users, accelerates learning, provides comprehensible feedback, and offers appropriate warnings when dangers emerge. Blackwell touches on some of these issues in the closing Gapminder example, but I was thirsty for more.

5.11 Commentary by Clive Richards

If I may be permitted a graphically inspired metaphor Alan Blackwell provides us with a neat pen sketch of that extensive scene called 'visual representation' (Blackwell 2011).

"Visualisation has a lot more to offer than most people are aware of today" we are told by Robert Kosara at the end of his commentary (Kosara 2010) on Stephen Few's related article on ' Data visualisation for human perception ' (Few 2010). Korsara is right, and Blackwell maps out the broad territory in which many of these visualisation offerings may be located. In this commentary I offer a few observations on some prominent features in that landscape: dynamics, picturing, semiotics and metaphor.

Ben Shneiderman's critique of Blackwell's piece points to a lack of attention to "... additional questions of changing state visualisations and interaction dynamics" (Shneiderman 2010). Indeed the possibilities offered by these additional questions present some exciting challenges for interaction designers - opportunities to create novel and effective combinations of visual with other sensory and motor experiences in dynamic operational contexts. Shneiderman suggests that: "These changing and interactive demands on designers invite creative expressions that are very different from design for static signs, printed diagrams, or interpretive art". This may be so up to a point, but here Shneinderman and I part company a little. The focus of Blackwell's essay is properly on the visual representation side of facilities available to interaction designers, and in that context he is quite right to give prominence to highly successful but static visual representation precedents, and also to point out the various specialist fields of endeavour in which they have been developed. Some of these representational approaches have histories reaching back thousands of years and are deeply embedded within our culture. It would be foolhardy to disregard conventions established in, say, the print domain, and to try to re-invent everything afresh for the screen, even if this were a practical proposition. Others have made arguments to support looking to historical precedents. For example Michael Twyman has pointed out that when considering typographic cueing and "... the problems of the electronic age ... we have much to learn from the manuscript age" (Twyman 1987, p5). He proposes that studying the early scribes' use of colour, spacing and other graphical devices can usefully inform the design of today's screen-based texts. And as Blackwell points out in his opening section on 'Typography and text' "most information on computer screen is still presented as text".

It is also sometimes assumed that the pictorial representation of a dynamic process is best presented dynamically. However it can be argued that the comic book convention of using a sequence of static frames is sometimes superior for focusing the viewer's attention on the critical events in a process, rather than using an animated sequence in which key moments may be missed. This is of course not to deny the immense value of the moving and interactive visual image in the right context. The Gapminder charts are a case in point (http://www.gapminder.org). Blackwell usefully includes one of these, but as a static presentation. These diagrams come to life and really tell their story through the clustering of balloons that inflate or deflate as they move about the screen when driven through simulated periods of time.

While designing a tool for engineers to learn about the operation and maintenance of an oil system for an aircraft jet engine, Detlev Fischer devised a series of interactive animations, called 'Cinegrams' to display in diagrammatic form various operating procedures (Fischer and Richards 1995). He used the cinematic techniques of time compression and expansion in one animated sequence to show how the slow accumulation of debris in an oil filter, over an extended period of time, would eventually create a blockage to the oil flow and trigger the opening of a by-pass device in split seconds. Notwithstanding my earlier comment about the potential superiority of the comic strip genre for displaying some time dependant processes this particular Cinegram proved very instructive for the targeted users. There are many other examples one could cite where dynamic picturing of this sort has been deployed to similarly good effect in interactive environments.

Shneinderman also comments that: "Blackwell's approach might be enriched by more discussion of visual representation in functional product design tied to meaningful tasks". An area I have worked in is the pictorial representation of engineering assemblies to show that which is normally hidden from view. Techniques to do this on the printed page include 'ghosting' (making occluding parts appear as if transparent), 'exploding' (showing components separately, set out in dis-assembly order along an axis) and cutting away (taking a slice out of an outer shell to reveal mechanisms beneath). All these three-dimensional picturing techniques were used by, if not actually invented by, Leonardo Da Vinci (Richards 2006). All could be enhanced by interactive viewer control - an area of further fruitful exploration for picturing purposes in technical documentation contexts.

Blackwell's section on 'Pictures' warns us that when considering picturing options to avoid the "resemblance fallacy" pointing out the role that convention plays, even in so called photo-realistic images. He also points out that viewers can be distracted from the message by incidental information in 'realistic' pictures. From my own work in the field I know that technical illustrators' synoptic black and white outline depictions are regarded as best for drawing the viewer's attention to the key features of a pictorial representation. Research in this area has shown that when using linear perspective type drawings the appropriate deployment of lines of varying 'weight', rather than of a single thickness, can have a significant effect on viewers' levels of understanding about what is depicted (Richards, Bussard and Newman 2007). This work was done specifically to determine an 'easy to read' visual representational style when manipulating on the screen images of CAD objects. The most effective convention was shown to be: thin lines for edges where both planes forming the edge are visible and thicker lines for edges where only one plane is visible - that is where an outline edge forms a kind of horizon to the object.

These line thickness conventions appear on the face of it to have little to do with how we normally perceive the world, and Blackwell tells us that: "A good pictorial representation need not simulate visual experience any more than a good painting of a unicorn need resemble an actual unicorn". And some particular representations of unicorns can aid our understanding of how to use semiotic theory to figure out how pictures may be interpreted and, importantly, sometimes misunderstood - as I shall describe in the following.

Blackwell mentions semiotics, almost in passing, however it can help unravel some of the complexities of visual representation. Evelyn Goldsmith uses a Charles Addams cartoon to explain the relevance of the 'syntactic', 'semantic' and 'pragmatic' levels of semiotic analysis when applied to pictures (Goldsmith 1978). The cartoon in question, like many of those by Charles Addams, has no caption. It shows two unicorns standing on a small island in the pouring rain forlornly watching the Ark sailing away into the distance. Goldsmith suggests that most viewers will have little trouble in interpreting the overlapping elements in the scene, for example that one unicorn is standing behind the other, nor any difficulty understanding that the texture gradient of the sea stands for a receding horizontal plane. These represent the syntactic level of interpretation. Most adults will correctly identify the various components of the picture at the semantic level, however Goldsmith proposes that a young child might mistake the unicorns for horses and be happy with 'boat' for the Ark. But at the pragmatic level of interpretation, unless a viewer of the picture is aware of the story of Noah's Ark, the joke will be lost  - the connection will not be made between the scene depicted in the drawing and the scarcity of unicorns. This reinforces the point that one should not assume that the understanding of pictures is straightforward. There is much more to it than a simple matter or recognition. This is especially the case when metaphor is involved in visual representation.

Blackwell's section on 'Visual metaphor' is essentially a critique of the use of "theories of visual metaphor" as an "approach to explaining the conventional Mackintosh/Windows 'desktop' ". His is a convincing argument but there is much more which may be said about the use of visual metaphor - especially to show that which otherwise cannot be pictured. In fact most diagrams employ a kind of spatial metaphor when not depicting physical arrangements, for example when using the branches of a tree to represent relations within a family (Richards 2002). The capability to represent the invisible is the great strength of the visual metaphor, but there are dangers, and here I refer back to semiotics and particularly the pragmatic level of analysis. One needs to know the story to get the picture.

In our parental home, one of the many books much loved by my two brothers and me, was The Practical Encyclopaedia for Children (Odhams circa 1948). In it a double page spread illustration shows the possible evolutionary phases of the elephant. These are depicted as a procession of animals in a primordial swamp cum jungle setting. Starting with a tiny fish and passing to a small aquatic creature climbing out of the water onto the bank the procession progresses on through eight phases of transformation, including the Moeritherium and the Paleomatodon, finishing up with the land-based giant of today's African Elephant. Recently one of my brothers confessed to me that through studying this graphical diorama he had believed as a child that the elephant had a life cycle akin to that of a frog. He had understood that the procession was a metaphor for time. He had just got the duration wrong - by several orders of magnitude. He also hadn't understood that each separate depiction was of a different animal. He had used the arguably more sophisticated concept that it was the same animal at different times and stages in its individual development.

Please forgive the cliché if I say that this anecdote clearly illustrates that there can be more to looking at a picture than meets the eye? Blackwell's essay provides some useful pointers for exploring the possibilities of this fascinating territory of picturing and visual representation in general.   

• Blackwell A 2011 'Visual representation' Interaction-Design.org
• Few S 2010 ' Data visualisation for human perception ' Interaction-Design.org
• Fischer D and Richards CJ 1995 'The presentation of time in interactive animated systems diagrams' In: Earnshaw RA and Vince JA (eds) Multimedia Systems and Applications London: Academic Press Ltd (pp141 - 159). ISBN 0-12-227740-6
• Goldsmith E 1978 An analysis of the elements affecting comprehensibility of illustrations intended as supportive of text PhD thesis (CNAA) Brighton Polytechnic
• Korsa R 2010 ' Commentary on Stephen Few's article : Data visualisation for human perception' Interaction-Design.org Odhams c. 1949 The practical encyclopaedia for children (pp 194 - 195)
• Richards CJ 2002 'The fundamental design variables of diagramming' In: Oliver P, Anderson M and Meyer B (eds) Diagrammatic representation and reasoning London: Springer Verlag (pp 85 - 102) ISBN 1-85233-242-5
• Richards CJ 2006 'Drawing out information - lines of communication in technical illustration' Information Design Journal 14 (2) 93 - 107
• Richards CJ, Bussard N, Newman R 2007 'Weighing up line weights: the value of differing line thicknesses in technical illustrations' Information Design Journal 15 (2) 171 - 181
• Shneiderman B 2011 'Commentary on Alan Blackwell's article: Visual representation' Interaction-Design.org
• Twyman M 1982 'The graphic representation of language' Information Design Journal 3 (1) 2 - 22

5.12 Commentary by Peter C-H. Cheng

Alan Blackwell has provided us with a fine introduction to the design of visual representations. The article does a great job in motivating the novice designer of visual representations to explore some of the fundamental issues that lurk just beneath the surface of creating effective representations.  Furthermore, he gives us all quite a challenge:

Alan, quite rightly, claims that we must consider the fundamental principles of symbolic correspondence, if we are to design new genres of visual representations beyond the common forms of displays and interfaces.  The report begins to equip the novice visual representation designer with an understanding of the nature of symbolic correspondence between the components of visual representations and the things they represent, whether objects, actions or ideas.  In particular, it gives a useful survey of how correspondence works in a range of representations and provides a systematic framework of how systems of correspondence can be applied to design. The interactive screen shot is an exemplary visual representation that vividly reveals the correspondence techniques used in each part of the example diagram.

However, suppose you really wished to rise to the challenge of creating novel visual representations, how far will a knowledge of the fundamentals of symbolic correspondence take you? Drawing on my studies of the role of diagrams in the history of science, experience of inventing novel visual representations and research on problem solving and learning with diagrams, from the perspective of Cognitive Science, my view is that such knowledge will be necessary but not sufficient for your endeavours.  So, what else should the budding visual representation designer consider? From the perspective of cognitive science there are at least three aspects that we may profitably target.

First, there is the knowledge of how human process information; specifically the nature of the human cognitive architecture. By this, I mean more than visual perception, but an understanding of how we mentally receive, store, retrieve, transform and transmit information. The way the mind deals with each of these basic types of information processing provides relevant constrains for the design of visual representations. For instance, humans often, perhaps even typically, encode concepts in the form of hierarchies of schemas, which are information structures that coordinate attributes that describe and differentiate classes of concepts. These hierarchies of schemas underpin our ability to efficiently generalize or specialize concepts. Hence, we can use this knowledge to consider whether particular forms of symbolic correspondence will assist or hinder the forms of inference that we hope the user of the representation may make. For example, are the main symbolic correspondences in a visual representation consistent with the key attributes of the schemas for the concepts being considered?

Second, it may be useful for the designer to consider the broader nature of the tasks that the user may wish to do with the designed representation.  Resource allocation, optimization, calculating quantities, inferences about of possible outcomes, classification, reasoning about extreme or special cases, and debugging: these are just a few of the many possibilities. These tasks are more generic than the information-oriented options considered in the 'design uses' column of Figure 27 in the article. They are worth addressing, because they provide constraints for the initial stages of representation design, by narrowing the search for what are likely to be effective correspondences to adopt. For example, if taxonomic classification is important, then separation and layering will be important correspondences; whereas magnitude calculations may demand scale mapping, Euclidian and metrical correspondences.

The third aspect concerns situations in which the visual representation must support not just a single task, but many diverse tasks. For example, a visual representation to help students learn about electricity will be used to explain the topology of circuits, make computations with electrical quantities, provide explanations of circuit behaviour (in terms of formal algebraic models and as qualitative causal models), facilitate fault finding or trouble shooting, among other activities. The creation of novel representations in such circumstances is perhaps one of the most challenging for designers. So, what knowledge can help? In this case, I advocate attempting to design representations on the basis of an analysis of the underlying conceptual structure of the knowledge of the target domain. Why? Because the nature of the knowledge is invariant across different classes of task. For example, for problem solving and learning of electricity, all the tasks depend upon the common fundamental conceptual structures of the domain that knit together the laws governing the physical properties of electricity and circuit topology. Hence, a representation that makes these concepts readily available through effective representation designed will probably be effective for a wide range of tasks.

In summary, it is desirable for the aspiring visual representation designer to consider symbolic correspondence, but I recommend they cast their net more widely for inspiration by learning about the human cognitive architecture, focusing on the nature of the task for which they are designing, and most critically thinking about the underlying conceptual structure of the knowledge of the target domain.

5.13 Commentary by Brad A. Myers

I have been teaching human-computer interaction to students with a wide range of backgrounds for many years. One of the most difficult areas for them to learn seems to be visual design. Students seem to quickly pick up rules like Nielsen's Heuristics for interaction (Nielsen & Molich, 1990), whereas the guidelines for visual design are much more subtle. Alan Blackwell's article presents many useful points, but a designer needs to know so much more! Whereas students can achieve competence at achieving Nielsen's "consistency and standards," for example, they struggle with selecting an appropriate representation for their information. And only a trained graphic designer is likely to be able to create an attractive and effective icon. Some people have a much better aesthetic sense, and can create much more beautiful and appropriate representations. A key goal of my introductory course, therefore, is to try to impart to the students how difficult it is to do visual design, and how wide the set of choices is. Studying the examples that Blackwell provides will give the reader a small start towards effective visual representations, but the path requires talent, study, and then iterative design and testing to evaluate and improve a design's success.

• Nielsen, J., & Molich, R. (1990). Heuristic evaluation of user interfaces. Paper presented at the Proc. ACM CHI'90 Conf, Seattle, WA, 249-256.
• See also: http://www.useit.com/papers/heuristic/heuristic_list.html

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Title: imrl: integrating visual, physical, temporal, and geometric representations for enhanced food acquisition.

Abstract: Robotic assistive feeding holds significant promise for improving the quality of life for individuals with eating disabilities. However, acquiring diverse food items under varying conditions and generalizing to unseen food presents unique challenges. Existing methods that rely on surface-level geometric information (e.g., bounding box and pose) derived from visual cues (e.g., color, shape, and texture) often lacks adaptability and robustness, especially when foods share similar physical properties but differ in visual appearance. We employ imitation learning (IL) to learn a policy for food acquisition. Existing methods employ IL or Reinforcement Learning (RL) to learn a policy based on off-the-shelf image encoders such as ResNet-50. However, such representations are not robust and struggle to generalize across diverse acquisition scenarios. To address these limitations, we propose a novel approach, IMRL (Integrated Multi-Dimensional Representation Learning), which integrates visual, physical, temporal, and geometric representations to enhance the robustness and generalizability of IL for food acquisition. Our approach captures food types and physical properties (e.g., solid, semi-solid, granular, liquid, and mixture), models temporal dynamics of acquisition actions, and introduces geometric information to determine optimal scooping points and assess bowl fullness. IMRL enables IL to adaptively adjust scooping strategies based on context, improving the robot's capability to handle diverse food acquisition scenarios. Experiments on a real robot demonstrate our approach's robustness and adaptability across various foods and bowl configurations, including zero-shot generalization to unseen settings. Our approach achieves improvement up to $35\%$ in success rate compared with the best-performing baseline.

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