graphical representation of data definition

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Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

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Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

Thanks very much for the information

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What Is Data Visualization: Brief Theory, Useful Tips and Awesome Examples

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By Al Boicheva

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Updated: June 23, 2022

To create data visualization in order to present your data is no longer just a nice to have skill. Now, the skill to effectively sort and communicate your data through charts is a must-have for any business in any field that deals with data. Data visualization helps businesses quickly make sense of complex data and start making decisions based on that data. This is why today we’ll talk about what is data visualization. We’ll discuss how and why does it work, what type of charts to choose in what cases, how to create effective charts, and, of course, end with beautiful examples.

So let’s jump right in. As usual, don’t hesitate to fast-travel to a particular section of your interest.

Article overview: 1. What Does Data Visualization Mean? 2. How Does it Work? 3. When to Use it? 4. Why Use it? 5. Types of Data Visualization 6. Data Visualization VS Infographics: 5 Main Differences 7. How to Create Effective Data Visualization?: 5 Useful Tips 8. Examples of Data Visualization

1. What is Data Visualization?

Data Visualization is a graphic representation of data that aims to communicate numerous heavy data in an efficient way that is easier to grasp and understand . In a way, data visualization is the mapping between the original data and graphic elements that determine how the attributes of these elements vary. The visualization is usually made by the use of charts, lines, or points, bars, and maps.

• Data Viz is a branch of Descriptive statistics but it requires both design, computer, and statistical skills.
• Aesthetics and functionality go hand in hand to communicate complex statistics in an intuitive way.
• Data Viz tools and technologies are essential for making data-driven decisions.
• It’s a fine balance between form and functionality.
• Every STEM field benefits from understanding data.

2. How Does it Work?

If we can see it, our brains can internalize and reflect on it. This is why it’s much easier and more effective to make sense of a chart and see trends than to read a massive document that would take a lot of time and focus to rationalize. We wouldn’t want to repeat the cliche that humans are visual creatures, but it’s a fact that visualization is much more effective and comprehensive.

In a way, we can say that data Viz is a form of storytelling with the purpose to help us make decisions based on data. Such data might include:

• Tracking sales
• Identifying trends
• Identifying changes
• Monitoring goals
• Monitoring results
• Combining data

3. When to Use it?

Data visualization is useful for companies that deal with lots of data on a daily basis. It’s essential to have your data and trends instantly visible. Better than scrolling through colossal spreadsheets. When the trends stand out instantly this also helps your clients or viewers to understand them instead of getting lost in the clutter of numbers.

With that being said, Data Viz is suitable for:

• Annual reports
• Presentations
• Social media micronarratives
• Informational brochures
• Trend-trafficking
• Candlestick chart for financial analysis
• Determining routes

Common cases when data visualization sees use are in sales, marketing, healthcare, science, finances, politics, and logistics.

4. Why Use it?

Short answer: decision making. Data Visualization comes with the undeniable benefits of quickly recognizing patterns and interpret data. More specifically, it is an invaluable tool to determine the following cases.

• Identifying correlations between the relationship of variables.
• Getting market insights about audience behavior.
• Determining value vs risk metrics.
• Monitoring trends over time.
• Examining rates and potential through frequency.
• Ability to react to changes.

5. Types of Data Visualization

As you probably already guessed, Data Viz is much more than simple pie charts and graphs styled in a visually appealing way. The methods that this branch uses to visualize statistics include a series of effective types.

Map visualization is a great method to analyze and display geographically related information and present it accurately via maps. This intuitive way aims to distribute data by region. Since maps can be 2D or 3D, static or dynamic, there are numerous combinations one can use in order to create a Data Viz map.

COVID-19 Spending Data Visualization POGO by George Railean

The most common ones, however, are:

• Regional Maps: Classic maps that display countries, cities, or districts. They often represent data in different colors for different characteristics in each region.
• Line Maps: They usually contain space and time and are ideal for routing, especially for driving or taxi routes in the area due to their analysis of specific scenes.
• Point Maps: These maps distribute data of geographic information. They are ideal for businesses to pinpoint the exact locations of their buildings in a region.
• Heat Maps: They indicate the weight of a geographical area based on a specific property. For example, a heat map may distribute the saturation of infected people by area.

Charts present data in the form of graphs, diagrams, and tables. They are often confused with graphs since graphs are indeed a subcategory of charts. However, there is a small difference: graphs show the mathematical relationship between groups of data and is only one of the chart methods to represent data.

Infographic Data Visualization by Madeline VanRemmen

With that out of the way, let’s talk about the most basic types of charts in data visualization.

They use a series of bars that illustrate data development.  They are ideal for lighter data and follow trends of no more than three variables or else, the bars become cluttered and hard to comprehend. Ideal for year-on-year comparisons and monthly breakdowns.

These familiar circular graphs divide data into portions. The bigger the slice, the bigger the portion. They are ideal for depicting sections of a whole and their sum must always be 100%. Avoid pie charts when you need to show data development over time or lack a value for any of the portions. Doughnut charts have the same use as pie charts.

They use a line or more than one lines that show development over time. It allows tracking multiple variables at the same time. A great example is tracking product sales by a brand over the years. Area charts have the same use as line charts.

Scatter Plot

These charts allow you to see patterns through data visualization. They have an x-axis and a y-axis for two different values. For example, if your x-axis contains information about car prices while the y-axis is about salaries, the positive or negative relationship will tell you about what a person’s car tells about their salary.

Unlike the charts we just discussed, tables show data in almost a raw format. They are ideal when your data is hard to present visually and aim to show specific numerical data that one is supposed to read rather than visualize.

Data Visualisation | To bee or not to bee by Aishwarya Anand Singh

For example, charts are perfect to display data about a particular illness over a time period in a particular area, but a table comes to better use when you also need to understand specifics such as causes, outcomes, relapses, a period of treatment, and so on.

6. Data Visualization VS Infographics

5 main differences.

They are not that different as both visually represent data. It is often you search for infographics and find images titled Data Visualization and the other way around. In many cases, however, these titles aren’t misleading. Why is that?

• Data visualization is made of just one element. It could be a map, a chart, or a table. Infographics , on the other hand, often include multiple Data Viz elements.
• Unlike data visualizations that can be simple or extremely complex and heavy, infographics are simple and target wider audiences. The latter is usually comprehensible even to people outside of the field of research the infographic represents.
• Interestingly enough, data Viz doesn’t offer narratives and conclusions, it’s a tool and basis for reaching those. While infographics, in most cases offer a story and a narrative. For example, a data visualization map may have the title “Air pollution saturation by region”, while an infographic with the same data would go “Areas A and B are the most polluted in Country C”.
• Data visualizations can be made in Excel or use other tools that automatically generate the design unless they are set for presentation or publishing. The aesthetics of infographics , however, are of great importance and the designs must be appealing to wider audiences.
• In terms of interaction, data visualizations often offer interactive charts, especially in an online form. Infographics, on the other hand, rarely have interaction and are usually static images.

While on topic, you could also be interested to check out these 50 engaging infographic examples that make complex data look great.

7. Tips to Create Effective Data Visualization

The process is naturally similar to creating Infographics and it revolves around understanding your data and audience. To be more precise, these are the main steps and best practices when it comes to preparing an effective visualization of data for your viewers to instantly understand.

1. Do Your Homework

Preparation is half the work already done. Before you even start visualizing data, you have to be sure you understand that data to the last detail.

Knowing your audience is undeniable another important part of the homework, as different audiences process information differently. Who are the people you’re visualizing data for? How do they process visual data? Is it enough to hand them a single pie chart or you’ll need a more in-depth visual report?

The third part of preparing is to determine exactly what you want to communicate to the audience. What kind of information you’re visualizing and does it reflect your goal?

And last, think about how much data you’ll be working with and take it into account.

2. Choose the Right Type of Chart

In a previous section, we listed the basic chart types that find use in data visualization. To determine best which one suits your work, there are a few things to consider.

• How many variables will you have in a chart?
• How many items will you place for each of your variables?
• What will be the relation between the values (time period, comparison, distributions, etc.)

With that being said, a pie chart would be ideal if you need to present what portions of a whole takes each item. For example, you can use it to showcase what percent of the market share takes a particular product. Pie charts, however, are unsuitable for distributions, comparisons, and following trends through time periods. Bar graphs, scatter plots,s and line graphs are much more effective in those cases.

Another example is how to use time in your charts. It’s way more accurate to use a horizontal axis because time should run left to right. It’s way more visually intuitive.

3. Sort your Data

Start with removing every piece of data that does not add value and is basically excess for the chart. Sometimes, you have to work with a huge amount of data which will inevitably make your chart pretty complex and hard to read. Don’t hesitate to split your information into two or more charts. If that won’t work for you, you could use highlights or change the entire type of chart with something that would fit better.

Tip: When you use bar charts and columns for comparison, sort the information in an ascending or a descending way by value instead of alphabetical order.

4. Use Colors to Your Advantage

In every form of visualization, colors are your best friend and the most powerful tool. They create contrasts, accents, and emphasis and lead the eye intuitively. Even here, color theory is important.

When you design your chart, make sure you don’t use more than 5 or 6 colors. Anything more than that will make your graph overwhelming and hard to read for your viewers. However, color intensity is a different thing that you can use to your advantage. For example, when you compare the same concept in different periods of time, you could sort your data from the lightest shade of your chosen color to its darker one. It creates a strong visual progression, proper to your timeline.

Things to consider when you choose colors:

• Different colors for different categories.
• A consistent color palette for all charts in a series that you will later compare.
• It’s appropriate to use color blind-friendly palettes.

5. Get Inspired

Always put your inspiration to work when you want to be at the top of your game. Look through examples, infographics, and other people’s work and see what works best for each type of data you need to implement.

This Twitter account Data Visualization Society is a great way to start. In the meantime, we’ll also handpick some amazing examples that will get you in the mood to start creating the visuals for your data.

8. Examples for Data Visualization

As another art form, Data Viz is a fertile ground for some amazing well-designed graphs that prove that data is beautiful. Now let’s check out some.

Dark Souls III Experience Data

We start with Meng Hsiao Wei’s personal project presenting his experience with playing Dark Souls 3. It’s a perfect example that infographics and data visualization are tools for personal designs as well. The research is pretty massive yet very professionally sorted into different types of charts for the different concepts. All data visualizations are made with the same color palette and look great in infographics.

My dark souls 3 playing data by Meng Hsiao Wei

Greatest Movies of all Time

Katie Silver has compiled a list of the 100 greatest movies of all time based on critics and crowd reviews. The visualization shows key data points for every movie such as year of release, oscar nominations and wins, budget, gross, IMDB score, genre, filming location, setting of the film, and production studio. All movies are ordered by the release date.

100 Greatest Movies Data Visualization by Katie Silver

The Most Violent Cities

Federica Fragapane shows data for the 50 most violent cities in the world in 2017. The items are arranged on a vertical axis based on population and ordered along the horizontal axis according to the homicide rate.

The Most Violent Cities by Federica Fragapane

Family Businesses as Data

These data visualizations and illustrations were made by Valerio Pellegrini for Perspectives Magazine. They show a pie chart with sector breakdown as well as a scatter plot for contribution for employment.

PERSPECTIVES MAGAZINE – Family Businesses by Valerio Pellegrini

Orbit Map of the Solar System

The map shows data on the orbits of more than 18000 asteroids in the solar system. Each asteroid is shown at its position on New Years’ Eve 1999, colored by type of asteroid.

An Orbit Map of the Solar System by Eleanor Lutz

The Semantics Of Headlines

Katja Flükiger has a take on how headlines tell the story. The data visualization aims to communicate how much is the selling influencing the telling. The project was completed at Maryland Institute College of Art to visualize references to immigration and color-coding the value judgments implied by word choice and context.

The Semantics of Headlines by Katja Flükiger

Moon and Earthquakes

This data visualization works on answering whether the moon is responsible for earthquakes. The chart features the time and intensity of earthquakes in response to the phase and orbit location of the moon.

Moon and Earthquakes by Aishwarya Anand Singh

Dawn of the Nanosats

The visualization shows the satellites launched from 2003 to 2015. The graph represents the type of institutions focused on projects as well as the nations that financed them. On the left, it is shown the number of launches per year and satellite applications.

WIRED UK – Dawn of the by Nanosats by Valerio Pellegrini

Final Words

Data visualization is not only a form of science but also a form of art. Its purpose is to help businesses in any field quickly make sense of complex data and start making decisions based on that data. To make your graphs efficient and easy to read, it’s all about knowing your data and audience. This way you’ll be able to choose the right type of chart and use visual techniques to your advantage.

You may also be interested in some of these related articles:

• Infographics for Marketing: How to Grab and Hold the Attention
• 12 Animated Infographics That Will Engage Your Mind from Start to Finish
• 50 Engaging Infographic Examples That Make Complex Ideas Look Great
• Good Color Combinations That Go Beyond Trends: Inspirational Examples and Ideas

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Al Boicheva

Al is an illustrator at GraphicMama with out-of-the-box thinking and a passion for anything creative. In her free time, you will see her drooling over tattoo art, Manga, and horror movies.

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Blog Graphic Design What is Data Visualization? (Definition, Examples, Best Practices)

What is Data Visualization? (Definition, Examples, Best Practices)

Written by: Midori Nediger Jun 05, 2020

Words don’t always paint the clearest picture. Raw data doesn’t always tell the most compelling story. 

The human mind is very receptive to visual information. That’s why data visualization is a powerful tool for communication.    

But if “data visualization” sounds tricky and technical don’t worry—it doesn’t have to be. 

This guide will explain the fundamentals of data visualization in a way that anyone can understand. Included are a ton of examples of different types of data visualizations and when to use them for your reports, presentations, marketing, and more.

Table of Contents

• What is data visualization?

What is data visualization used for?

Types of data visualizations.

• How to present data visually  (for businesses, marketers, nonprofits, and education)
• Data visualization examples

Data visualization is used everywhere. 

Businesses use data visualization for reporting, forecasting, and marketing. 

CREATE THIS REPORT TEMPLATE

Nonprofits use data visualizations to put stories and faces to numbers. 

Source:  Bill and Melinda Gates Foundation

Scholars and scientists use data visualization to illustrate concepts and reinforce their arguments.

CREATE THIS MIND MAP TEMPLATE

Reporters use data visualization to show trends and contextualize stories. 

While data visualizations can make your work more professional, they can also be a lot of fun.

What is data visualization? A simple definition of data visualization:

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart , infographic , diagram or map. 

The field of data visualization combines both art and data science. While a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. 

Data, especially a lot of data, can be difficult to wrap your head around. Data visualization can help both you and your audience interpret and understand data. 

Data visualizations often use elements of visual storytelling to communicate a message supported by the data. 

There are many situations where you would want to present data visually. 

Data visualization can be used for:

• Making data engaging and easily digestible
• Identifying trends and outliers within a set of data
• Telling a story found within the data
• Reinforcing an argument or opinion
• Highlighting the important parts of a set of data

Let’s look at some examples for each use case.

1. Make data digestible and easy to understand

Often, a large set of numbers can make us go cross-eyed. It can be difficult to find the significance behind rows of data. 

Data visualization allows us to frame the data differently by using illustrations, charts, descriptive text, and engaging design. Visualization also allows us to group and organize data based on categories and themes, which can make it easier to break down into understandable chunks. 

Related : How to Use Data Visualization in Your Infographics

For example, this infographic breaks down the concept of neuroplasticity in an approachable way:

Source: NICABM

The same goes for complex, specialized concepts. It can often be difficult to break down the information in a way that non-specialists will understand. But an infographic that organizes the information, with visuals, can demystify concepts for novice readers.

CREATE THIS INFOGRAPHIC TEMPLATE

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2. Identify trends and outliers

If you were to sift through raw data manually, it could take ages to notice patterns, trends or outlying data. But by using data visualization tools like charts, you can sort through a lot of data quickly. 

Even better, charts enable you to pick up on trends a lot quicker than you would sifting through numbers.

For example, here’s a simple chart generated by Google Search Console that shows the change in Google searches for “toilet paper”. As you can see, in March 2020 there was a huge increase in searches for toilet paper:

Source: How to Use SEO Data to Fuel Your Content Marketing Strategy in 2020

This chart shows an outlier in the general trend for toilet paper-related Google searches. The reason for the outlier? The outbreak of COVID-19 in North America. With a simple data visualization, we’ve been able to highlight an outlier and hint at a story behind the data. 

Uploading your data into charts, to create these kinds of visuals is easy. While working on your design in the editor, select a chart from the left panel. Open the chart and find the green IMPORT button under the DATA tab. Then upload the CSV file and your chart automatically visualizes the information. 

3. Tell a story within the data

Numbers on their own don’t tend to evoke an emotional response. But data visualization can tell a story that gives significance to the data. 

Designers use techniques like color theory , illustrations, design style and visual cues to appeal to the emotions of readers, put faces to numbers, and introduce a narrative to the data. 

Related : How to Tell a Story With Data (A Guide for Beginners)

For example, here’s an infographic created by World Vision. In the infographics, numbers are visualized using illustrations of cups. While comparing numbers might impress readers, reinforcing those numbers with illustrations helps to make an even greater impact. 

Source: World Vision

Meanwhile, this infographic uses data to draw attention to an often overlooked issue:

Read More:  The Coronavirus Pandemic and the Refugee Crisis

4. Reinforce an argument or opinion

When it comes to convincing people your opinion is right, they often have to see it to believe it. An effective infographic or chart can make your argument more robust and reinforce your creativity. 

For example, you can use a comparison infographic to compare sides of an argument, different theories, product/service options, pros and cons, and more. Especially if you’re blending data types.

5. Highlight an important point in a set of data

Sometimes we use data visualizations to make it easier for readers to explore the data and come to their own conclusions. But often, we use data visualizations to tell a story, make a particular argument, or encourage readers to come to a specific conclusion. 

Designers use visual cues to direct the eye to different places on a page. Visual cues are shapes, symbols, and colors that point to a specific part of the data visualization, or that make a specific part stand out.

For example, in this data visualization, contrasting colors are used to emphasize the difference in the amount of waste sent to landfills versus recycled waste:

Here’s another example. This time, a red circle and an arrow are used to highlight points on the chart where the numbers show a drop: 

Highlighting specific data points helps your data visualization tell a compelling story.

6. Make books, blog posts, reports and videos more engaging

At Venngage, we use data visualization to make our blog posts more engaging for readers. When we write a blog post or share a post on social media, we like to summarize key points from our content using infographics. 

The added benefit of creating engaging visuals like infographics is that it has enabled our site to be featured in publications like The Wall Street Journal , Mashable , Business Insider , The Huffington Post and more. 

That’s because data visualizations are different from a lot of other types of content people consume on a daily basis. They make your brain work. They combine concrete facts and numbers with impactful visual elements. They make complex concepts easier to grasp. 

Here’s an example of an infographic we made that got a lot of media buzz:

Read the Blog Post: Every Betrayal Ever in Game of Thrones

We created this infographic because a bunch of people on our team are big Game of Thrones fans and we wanted to create a visual that would help other fans follow the show. Because we approached a topic that a lot of people cared about in an original way, the infographic got picked up by a bunch of media sites. 

Whether you’re a website looking to promote your content, a journalist looking for an original angle, or a creative building your portfolio, data visualizations can be an effective way to get people’s attention.

Data visualizations can come in many different forms. People are always coming up with new and creative ways to present data visually. 

Generally speaking, data visualizations usually fall under these main categories:

An infographic is a collection of imagery, charts, and minimal text that gives an easy-to-understand overview of a topic. 

While infographics can take many forms, they can typically be categorized by these infographic types:

• Statistical infographics
• Informational infographics
• Timeline infographics
• Process infographics
• Geographic infographics
• Comparison infographics
• Hierarchical infographics
• List infographics
• Resume infographics

Read More: What is an Infographic? Examples, Templates & Design Tips

Charts 

In the simplest terms, a chart is a graphical representation of data. Charts use visual symbols like line, bars, dots, slices, and icons to represent data points. 

Some of the most common types of charts are:

• Bar graphs /charts
• Line charts
• Bubble charts
• Stacked bar charts
• Word clouds
• Pictographs
• Area charts
• Scatter plot charts
• Multi-series charts

The question that inevitably follows is: what type of chart should I use to visualize my data? Does it matter?

Short answer: yes, it matters. Choosing a type of chart that doesn’t work with your data can end up misrepresenting and skewing your data. 

For example: if you’ve been in the data viz biz for a while, then you may have heard some of the controversy surrounding pie charts. A rookie mistake that people often make is using a pie chart when a bar chart would work better. 

Pie charts display portions of a whole. A pie chart works when you want to compare proportions that are substantially different. Like this:

CREATE THIS CHART TEMPLATE

But when your proportions are similar, a pie chart can make it difficult to tell which slice is bigger than the other. That’s why, in most other cases, a bar chart is a safer bet.

Here is a cheat sheet to help you pick the right type of chart for your data:

Want to make better charts? Make engaging charts with Venngage’s Chart Maker .

Related : How to Choose the Best Types of Charts For Your Data

Similar to a chart, a diagram is a visual representation of information. Diagrams can be both two-dimensional and three-dimensional. 

Some of the most common types of diagrams are:

• Venn diagrams
• Tree diagrams
• SWOT analysis
• Fishbone diagrams
• Use case diagrams

Diagrams are used for mapping out processes, helping with decision making, identifying root causes, connecting ideas, and planning out projects.

CREATE THIS DIAGRAM TEMPLATE

Want to make a diagram ? Create a Venn diagram and other visuals using our free Venn Diagram Maker .

A map is a visual representation of an area of land. Maps show physical features of land like regions, landscapes, cities, roads, and bodies of water. 

Source: National Geographic

A common type of map you have probably come across in your travels is a choropleth map . Choropleth maps use different shades and colors to indicate average quantities. 

For example, a population density map uses varying shades to show the difference in population numbers from region to region:

Create your own map for free with Venngage’s Map Maker .

How to present data visually (data visualization best practices)

While good data visualization will communicate data or information clearly and effectively, bad data visualization will do the opposite. Here are some practical tips for how businesses and organizations can use data visualization to communicate information more effectively. 

Not a designer? No problem. Venngage’s Graph Maker  will help you create better graphs in minutes.

1. Avoid distorting the data

This may be the most important point in this whole blog post. While data visualizations are an opportunity to show off your creative design chops, function should never be sacrificed for fashion. 

The chart styles, colors, shapes, and sizing you use all play a role in how the data is interpreted. If you want to present your data accurately and ethically, then you need to take care to ensure that your data visualization does not present the data falsely. 

There are a number of different ways data can be distorted in a chart. Some common ways data can be distorted are:

• Making the baselines something other than 0 to make numbers seem bigger or smaller than they are – this is called “truncating” a graph
• Compressing or expanding the scale of the Y-axis to make a line or bar seem bigger or smaller than it should be
• Cherry picking data so that only the data points you want to include are on a graph (i.e. only telling part of the story)
• Using the wrong type of chart, graph or diagram for your data
• Going against standard, expected data visualization conventions

Because people use data visualizations to reinforce their opinions, you should always read data visualizations with a critical eye. Often enough, writers may be using data visualization to skew the data in a way that supports their opinions, but that may not be entirely truthful.

Read More: 5 Ways Writers Use Graphs To Mislead You

Want to create an engaging line graph? Use Venngage’s Line Graph Maker to create your own in minutes.

2. Avoid cluttering up your design with “chartjunk”

When it comes to best practices for data visualization, we should turn to one of the grandfather’s of data visualization: Edward Tufte. He coined the term “ chartjunk ”, which refers to the use of unnecessary or confusing design elements that skews or obscures the data in a chart. 

Here’s an example of a data visualization that suffers from chartjunk:

Source: ExcelUser

In this example, the image of the coin is distracting for readers trying to interpret the data. Note how the fonts are tiny – almost unreadable. Mistakes like this are common when a designers tries to put style before function. 

Read More : The Worst Infographics of 2020 (With Lessons for 2021)

3. Tell a story with your data

Data visualizations like infographics give you the space to combine data and narrative structure in one page. Visuals like icons and bold fonts let you highlight important statistics and facts.

For example, you could customize this data visualization infographic template to show the benefit of using your product or service (and post it on social media):

USE THIS TEMPLATE

  This data visualization relies heavily on text and icons to tell the story of its data:

This type of infographic is perfect for those who aren’t as comfortable with charts and graphs. It’s also a great way to showcase original research, get social shares and build brand awareness.

4. Combine different types of data visualizations

While you may choose to keep your data visualization simple, combining multiple types of charts and diagrams can help tell a more rounded story.

Don’t be afraid to combine charts, pictograms and diagrams into one infographic. The result will be a data visualization infographic that is engaging and rich in visual data.

Design Tip: This data visualization infographic would be perfect for nonprofits to customize and include in an email newsletter to increase awareness (and donations).

Or take this data visualization that also combines multiple types of charts, pictograms, and images to engage readers. It could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more:

Design Tip: This infographic could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more.

Make your own bar graph in minutes with our free Bar Graph Maker .

5. Use icons to emphasize important points

Icons are perfect for attracting the eye when scanning a page. (Remember: use visual cues!)

If there are specific data points that you want readers to pay attention to, placing an icon beside it will make it more noticeable:

Design Tip: This infographic template would work well on social media to encourage shares and brand awareness.

You can also pair icons with headers to indicate the beginning of a new section.

Meanwhile, this infographic uses icons like bullet points to emphasize and illustrate important points. 

Design Tip: This infographic would make a great sales piece to promote your course or other service.  

6. Use bold fonts to make text information engaging

A challenge people often face when setting out to visualize information is knowing how much text to include. After all, the point of data visualization is that it presents information visually, rather than a page of text. 

Even if you have a lot of text information, you can still create present data visually. Use bold, interesting fonts to make your data exciting. Just make sure that, above all else, your text is still easy to read.

This data visualization uses different fonts for the headers and body text that are bold but clear. This helps integrate the text into the design and emphasizes particular points:

Design Tip: Nonprofits could use this data visualization infographic in a newsletter or on social media to build awareness, but any business could use it to explain the need for their product or service. 

As a general rule of thumb, stick to no more than three different font types in one infographic.

This infographic uses one font for headers, another font for body text, and a third font for accent text. 

Read More: How to Choose Fonts For Your Designs (With Examples)

Design Tip: Venngage has a library of fonts to choose from. If you can’t find the icon you’re looking for , you can always request they be added. Our online editor has a chat box with 24/7 customer support.

7. Use colors strategically in your design

In design, colors are as functional as they are fashionable. You can use colors to emphasize points, categorize information, show movement or progression, and more. 

For example, this chart uses color to categorize data:

Design Tip : This pie chart can actually be customized in many ways. Human resources could provide a monthly update of people hired by department, nonprofits could show a breakdown of how they spent donations and real estate agents could show the average price of homes sold by neighbourhood.

You can also use light colored text and icons on dark backgrounds to make them stand out. Consider the mood that you want to convey with your infographic and pick colors that will reflect that mood. You can also use contrasting colors from your brand color palette.

This infographic template uses a bold combination of pinks and purples to give the data impact:

Read More: How to Pick Colors to Captivate Readers and Communicate Effectively

8. Show how parts make up a whole

It can be difficult to break a big topic down into smaller parts. Data visualization can make it a lot easier for people to conceptualize how parts make up a whole.

Using one focus visual, diagram or chart can convey parts of a whole more effectively than a text list can. Look at how this infographic neatly visualizes how marketers use blogging as part of their strategy:

Design Tip: Human resources could use this graphic to show the results of a company survey. Or consultants could promote their services by showing their success rates.

Or look at how this infographic template uses one focus visual to illustrate the nutritional makeup of a banana:

CREATE THIS FLYER TEMPLATE

9. Focus on one amazing statistic

If you are preparing a presentation, it’s best not to try and cram too many visuals into one slide. Instead, focus on one awe-inspiring statistic and make that the focus of your slide.

Use one focus visual to give the statistic even more impact. Smaller visuals like this are ideal for sharing on social media, like in this example:

Design Tip: You can easily swap out the icon above (of Ontario, Canada) using Venngage’s drag-and-drop online editor and its in-editor library of icons. Click on the template above to get started.

This template also focuses on one key statistic and offers some supporting information in the bar on the side:

10. Optimize your data visualization for mobile

Complex, information-packed infographics are great for spicing up reports, blog posts, handouts, and more. But they’re not always the best for mobile viewing. 

To optimize your data visualization for mobile viewing, use one focus chart or icon and big, legible font. You can create a series of mobile-optimized infographics to share multiple data points in a super original and attention-grabbing way.

For example, this infographic uses concise text and one chart to cut to the core message behind the data:

CREATE THIS SOCIAL MEDIA TEMPLATE

Some amazing data visualization examples

Here are some of the best data visualization examples I’ve come across in my years writing about data viz. 

Evolution of Marketing Infographic

Graphic Design Trends Infographic

Stop Shark Finning Nonprofit Infographic

Source: Ripetungi

Coronavirus Impact on Environment Data Visualization

What Disney Characters Tell Us About Color Theory

World’s Deadliest Animal Infographic

Source: Bill and Melinda Gates Foundation

The Secret Recipe For a Viral Creepypasta

Read More: Creepypasta Study: The Secret Recipe For a Viral Horror Story

The Hero’s Journey Infographic

Read More: What Your 6 Favorite Movies Have in Common

Emotional Self Care Guide Infographic

Source: Carley Schweet

Want to look at more amazing data visualization? Read More: 50+ Infographic Ideas, Examples & Templates for 2020 (For Marketers, Nonprofits, Schools, Healthcare Workers, and more)

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Introduction to Graphs

Table of Contents

15 December 2020                 

Read time: 6 minutes

Introduction

What are graphs?

What are the different types of data?

What are the different types of graphical representations?

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation.

The word data came from the Latin word Datum which means “something given”

After a research question is developed, data is being collected continuously through observation. Then it is organized, summarized, classified, and then represented graphically.

Differences between Data and information: Data is the raw fact without any add on but the information is the meaning derived from data.

Introduction to Graphs-PDF

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation. Here is a downloadable PDF to explore more.

• Line and Bar Graphs Application
• Graphs in Mathematics & Statistics

What are the different Types of Data?

There are two types of Data :

Quantitative

The data which are statistical or numerical are known as Quantitive data. Quantitive data is generated through. Quantitative data is also known as Structured data. Experiments, Tests, Surveys, Market Report.

Quantitive data is again divided into Continuous data and Discrete data.

Continuous Data

Continuous data is the data which can have any value. That means Continuous data can give infinite outcomes so it should be grouped before representing on a graph.

• The speed of a vehicle as it passes a checkpoint
• The mass of a cooking apple
• The time taken by a volunteer to perform a task

Discrete Data

Discrete data can have certain values. That means only a finite number can be categorized as discrete data.

• Numbers of cars sold at a dealership during a given month
• Number of houses in certain block
• Number of fish caught on a fishing trip
• Number of complaints received at the office of airline on a given day
• Number of customers who visit at bank during any given hour
• Number of heads obtained in three tosses of a coin

Differences between Discrete and Continuous data

• Numerical data could be either discrete or continuous
• Continuous data can take any numerical value (within a range); For example, weight, height, etc.
• There can be an infinite number of possible values in continuous data
• Discrete data can take only certain values by finite ‘jumps’, i.e., it ‘jumps’ from one value to another but does not take any intermediate value between them (For example, number of students in the class)

Qualitative

Data that deals with description or quality instead of numbers are known as Quantitative data. Qualitative data is also known as unstructured data. Because this type of data is loosely compact and can’t be analyzed conventionally.

Different Types of Graphical Representations

There are many types of graph we can use to represent data. They are as follows,

A bar graph or chart is a way to represent data by rectangular column or bar. The heights or length of the bar is proportional to the values.

A line graph is a type of graph where the information or data is plotted as some dots which are known as markers and then they are added to each other by a straight line.

The line graph is normally used to represent the data that changes over time.

A histogram graph is a graph where the information is represented along with the height of the rectangular bar. Though it does look like a bar graph, there is a fundamental difference between them. With the histogram, each column represents a range of quantitative data when a bar graph represents categorical variables.

The other name of the pie chart is a circle graph. It is a circular chart where numerical information represents as slices or in fractional form or percentage where the whole circle is 100%.

• Stem and leaf plot

The stem and leaf plot is a way to represents quantitative data according to frequency ranges or frequency distribution.

In the stem and leaf plot, each data is split into stem and leaf, which is 32 will be split into 3 stems and 2 leaves.

Frequency table: Frequency means the number of occurrences of an event. A frequency distribution table is a graph or chart which shows the frequency of events. It is denoted as ‘f’ .

Pictograph or Pictogram is the earliest way to represents data in a pictorial form or by using symbols or images. And each image represents a particular number of things.

According to the above-mentioned Pictograph, the number of Appels sold on Monday is 6x2=12.

• Scatter diagrams

Scatter diagram or scatter plot is a way of graphical representation by using cartesian coordinates of two variables. The plot shows the relationship between two variables. Below there is a data table as well as a Scattergram as per the given data.

What is the meaning of Graphical representation?

Graphical representation is a way to represent and analyze quantitive data. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables.

Principles of graphical representation

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin.

On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value.

When X-axis and y-axis intersected each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV.

The location on the coordinate plane is known as the ordered pair and it is written as (x,y). That means the first value will be on the x-axis and the second one is on the y-axis. When we will plot any coordinate, we always have to start counting from the origin and have to move along the x-axis, if it is positive then to the right side, and if it is negative then to the left side. Then from the x-axis, we have to plot the y’s value, which means we have to move up for positive value or down if the value is negative along with the y-axis.

In the following graph, 1st ordered pair (2,3) where both the values of x and y are positive and it is on quadrant I. 2nd ordered pair (-3,1), here the value of x is negative and value of y is positive and it is in quadrant II. 3rd ordered pair (-1.5, -2.5), here the value of x as well as y both are Negative and in quadrant III.

Methods of representing a frequency distribution

There are four methods to represent a frequency distribution graphically. These are,

• Smoothed Frequency graph
• Cumulative frequency graph or Ogive.
• Pie diagram.

Advantages and Disadvantages of Graphical representation of data

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represents it graphically.

You may also like:

• Graphing a Quadratic Function
• Empirical Relationship Between Mean, Median, and Mode

Not only in mathematics but almost in every field the graph is a very important way to store, analyze, and represents information. After any research work or after any survey the next step is to organize the observation or information and plotting them on a graph paper or plane. The visual representation of information makes the understanding of crucial components or trends easier.

A huge amount of data can be store or analyze in a small space.

The graphical representation of data helps to decide by following the trend.

A complete Idea: Graphical representation constitutes a clear and comprehensive idea in the minds of the audience. Reading a large number (say hundreds) of pages may not help to make a decision. Anyone can get a clear idea just by looking into the graph or design.

Graphs are a very conceptual topic, so it is essential to get a complete understanding of the concept. Graphs are great visual aids and help explain numerous things better, they are important in everyday life. Get better at graphs with us, sign up for a free trial . 

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Frequently Asked Questions (FAQs)

What is data.

Data are characteristics or information, usually numerical, that are collected through observation.

How do you differentiate between data and information?

Data is the raw fact without any add on but the information is the meaning derived from data.

What are the types of data?

There are two types of Data:

What are the ways to represent data?

Tables, charts and graphs are all ways of representing data , and they can be used for two broad purposes. The first is to support the collection, organisation and analysis of data as part of the process of a scientific study.

- Tables, charts and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organisation and analysis of data as part of the process of a scientific study.

What are the different types of graphs?

Different types of graphs include:

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Overview of Data Visualization

Shanghai University of Engineering Science, Shanghai, China

This chapter will first address data visualization and then discuss the relationship between data visualization and aesthetics. It discusses the definition of data and information and the forms and characteristics of traditional data visualization, emphasizes on understanding of meaning of data in effectiveness and efficiency. And then this chapter outlines some key data visualizations, which includes Trees, Scatter plots, Charts, Tables, Diagram, Graphic, Waveform, Simulation and Volume.

The Concept of Data Visualization

The term “data visualization” has a long history dated back the 2nd century AD. At the ancient society, drawings and other visual representations were used to investigate the world and also to record the historical events. Data visualization has significantly contributed to invention and discovery throughout human history (Crapo, Waisel, Wallace, & Willemain, 2000 ). The invention of computer technology makes the huge change on the way of visual representation of data. Data analyst has become quicker and more accuracy using computer graphical data visualization. Data visualization has become an important part of research in many fields including algorithms, human perception, animation, computer vision and so on. Data visualization is usually associated with the field of computer science in the contemporary society. As an emerging field, it is considered a sub-classification of visualization and is regarded as “the science of visual representation of ‘data’” (Friendly, 2009 , p. 2). The technology of data visualization has evolved from using hand drawing in the earliest stages, to “photo-etching”, to using computer technology, such as computing graphics and software (Friendly, 2009 ). In particular, the development of computer software has advanced the application of data visualization, allowing users to manipulate a substantial amount of data for exploration and analysis in an easier and more affordable way. In this book, data visualization that focuses on the communication for understanding data rather than any other approaches is considered as traditional data visualization.

Traditional data visualization has a number of advantages. To begin with, it has the ability to represent a vast amount of data immediately. Secondly, it enables viewers to identify emergent properties (e.g. patterns) in the data immediately for formulating new insights. The third advantage is that it can be used for product quality control where the immediate identification of problems is made possible through the data analysis. The fourth is that it enhances the understanding of large-scale and small-scale data. In this regard, Gray, Mayer, and Hughes (cited in Ware, 2012 , p. 3) suggest that data visualization assists in the constructing of hypotheses.

Data visualization often results in graphical images of data or concepts, which assists making decisions (Ware, 2012 ). The development of computing technology facilitates data visualization, identifying useful information or deriving insights from the graphical images. The importance of data visualization is described as follows:

The success of data visualization is due to the soundness of the basic idea behind it: the use of computer-generated images to gain insight and knowledge from data and its inherent patterns and relationships. A second premise is the utilization of the broad bandwidth processes, and simulations involving data sets from diverse scientific disciplines and large collections of abstract data from many sources. (Post, Nielson, & Bonneau, 2003 , back cover)

This statement emphasizes that data visualization as scientific research relies on computing technology and its utilization for the process of information. For this research, it is necessary to initially define the concept of data visualization in order to explore and identify the key forms and characteristics for designing a theoretical framework for Taoist data visualization, which will be discussed in Chap. 10.1007/978-981-15-5069-0_7.

The Definition of Data Visualization

The term “data visualization” can be defined in several ways. Most definitions focus on the connection between data and computer technology in order to transform data into a visual or sonic form. Card, Mackinlay, and Schneiderman ( 1999 ) define data visualization as “the use of computer-supported, interactive, visual representations of data to amplify cognition” (p. 6). Manovich ( 2010 ) defines it as “a transformation of quantified data which is not visual into a visual representation” (p. 20). According to Friendly ( 2009 ), data visualization refers to “information which has been abstracted in some schematic form, including attributes or variables for the units of information” (p. 2). Data visualization involves an information exchange that includes the messenger, the receiver, and the message (Kirk, 2012 ). Kirk ( 2012 ) defines data visualization as “the representation and presentation of data that exploits our visual perception abilities on order to amplify cognition” (p. 17). It emphasizes that the design of data visualization requires representing data in an effective and efficient form. Visual representation of data is a key element for these definitions. The purpose of data visualization identifies patterns inside the graphic through exploring and analyzing data. Thus, Bikakis ( 2018 ) defines data visualization as the following:

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.

From mathematical perspective, it refers to an “understanding of the real number line, time, measurement, and estimation” as well as an “understanding of ratio concepts, notably fractions, proportions, percentages, and probabilities” (Reyna, Nelson, Han, &, Dieckmann, 2009 ).

Numerous visualization software or applications have been developed and the main task is exploring, visualizing and analyzing data. In modern time, data visualization involves four aspects, which refer to real-time interaction, on-the-fly processing, visual scalability and user assistance and personalization (Bikakis, 2018 ). As Bikakis ( 2018 ) explains that real-time interaction requires efficient and scalable techniques that “should support the interaction with billion objects datasets, while maintaining the system response in the range of a few milliseconds.”(p. 2). For on-the-fly processing, the support of on-the-fly visualizations over large and dynamic sets of volatile raw (i.e., not preprocessed) data is required” (Bikakis, 2018 , p.2). When addressing information overloading problems, effective data abstraction mechanism is necessary. A key feature of the modern visualization is user comprehension and customization capabilities to various user-defined exploration scenarios and preferences (Bikakis, 2018 ).

Data visualization can be categorized into two major sub-fields: information visualization and scientific visualization (Marai, 2010 ; Post et al., 2003 ). Information visualization is used to visually represent abstract data, such as business data (Card, Mackinlay, & Schneiderman, 1999 ; Spence, 2007 ), while scientific visualization represents scientific data, which are usually physically based (e.g. the human body, the environment or the atmosphere) (Spence, 2007 ). Both information and scientific visualization focus on how to transform data into a visual form, to become understandable information for gaining insight and knowledge. Figure  2.1 presents the fundamental process of data visualization, in which data in any form can be transformed into graphical images. When a user reads or looks at a graphical image, the image is interpreted through the human cognitive system for the acquisition of insight or the apprehension of useful information.

The process of data visualization, illustrated by Yaqin Fu

Central to the process of data visualization is the transformation of data into information. Understanding the differences between the concepts of data and information, as well as their relationship is crucial to understanding the process of data visualization. The difference between data and information is outlined in the next section.

The Definition of Data and Information

The definition of data may vary depending on the context of the disciplines and the application of data. Data usually refers to raw, unprocessed information that is not recognized as having any meaning. Data can be organized in a variety of forms, such as name, numbers, age, signs, characters or symbols (Bellinger, Castro, & Mills, 2004 ; Yeung, 1998 ; Zins, 2007 ) and files, reports, graphs in a business context (“Concepts in information and processing,” n.d.). According to Ackoff ( 1989 ), data is symbols, while other researchers define data as “computerized representations of models and attributes of real or simulated entities” (Chen, 2005 , p. 1). Data is an integral part of the culture in the contemporary information society. Human are confronted with the flood of data every seconds, such as World Wide Web, cellular calls, networks of smart home applications (Pedrycz, 2005 ). Data is raw and simply exists. Data “has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself” (Bellinger et al., 2004 ).

There are two types of data, which are primary data and secondary data (Agarwal, 2006 ). If the data are “collected from the units or individual respondents directly for the purpose of certain study of information” (Agarwal, 2006 , p. 3), it is called primary data. For example, the primary data can be the data collected from a census study or an interview about enquiry made from tax payers. Many examples can be found in research, in particular in scientific study. The primary data can be numbers and people, for example, when “an experiment is conducted to know the effect of certain fertilizer doses on the yield or the effect of a drug on the patients, the observations taken on each plot or patient” are the primary data (Agarwal, 2006 , p. 3). Agarwal ( 2006 ) defines the secondary data as the information contained in data, which “used to again from records, processed and statistically analyzed to extract some information for other purpose” (p. 3). It emphasizes that the secondary data is collected and statistically purposed by certain people or agency. Therefore, the secondary data can be obtained from year books, census reports, research reports or survey reports for the scientific studies. In this sense, the primary data can be considered as raw data and the secondary data can be considered as information.

In this book, “data” is defined as it is used in the fields of computing science and information science, as “unprocessed information” in the form of numbers or binary codes, which are the quantities stored and transmitted as electrical signals in computers. In this sense, data can be measured, explored, manipulated or retrieved. The data discussed in this book includes both primary data and secondary data.

Data and information have a very close relationship as data is usually defined as “unprocessed information” and information considered “processed data” (Hey, 2004 ; Zins, 2007 ). Data itself has no value, significance or meaning until it has been processed into a form of information that can be understand by humans (Bellinger et al., 2004 ; Bernstein, 2011 ; Zins, 2007 ). Many different forms have been used to represent data. It depends on whether the data is geometric or symbolic, or whether it is static or dynamic. It also depends on the kinds of purposes the data visualization will serve.

Similarly to data, the definition of “information” depends on the discipline and the context. In information science, the term normally refers to “processed data” that has meaning and can be understood by humans (Ackoff, 1989 ; Davis & Olson, 1985 ; “How to define data, information and knowledge,” n.d.). Information is considered “collections of data” (Zins, 2007 , The Panel’s Definitions section, para. 32) that have been “collected and processed into numbers, artificial and natural languages, graphic objects that convey significance and meaning” (Zins, 2007 , The Panel’s Definitions section, para. 32). Information can be data that “are processed to be useful, providing answers to ‘who’, ‘what’, ‘where’, and ‘when’ questions” (Chen, 2005 , p. 1). Bellinger et al. ( 2004 ) argue that

information is data that has been given meaning by way of relational connection. This “meaning” can be useful, but does not have to be. In computer parlance, a relational database makes for information from the data stored within it.

Therefore, information is considered as useful data, which people can recognize and understand it. Chen ( 2005 ) emphasizes that “data that represents the results of a computational process, such as statistical analysis, for assigning meanings to the data, or the transcripts of some meanings assigned by human beings” (p. 1).

The Forms of Traditional Data Visualization

To understand traditional data visualization, it discusses the main forms of traditional data visualization in this section. The different forms of data visualization present a different visual effect, which can help to identify the problems or issues. It is also important to select the types of data visualization for a variety of disciplines or research fields. Although information visualization and scientific visualization are two different research fields, the forms of visualization are certain similarity. The visual forms of two visualizations are often overlaps. Table  2.1 presents the main forms of data visualization. Those forms of representation are also called “visualization techniques” (Cawthon & Moere, 2007 ; Fayyad & Grinstein, 2002 ), which have been developed to optimize selecting and creating the most effective and efficient transformation of data into information (Senay & Ignatius, 1990 ).

Table 2.1

The forms of traditional data visualization. It shows the types and common forms of data visualization

As represented within the table, the common forms of data visualization refer to trees, scatter plots, charts, tables, diagrams, and graphs. Selecting forms for data visualization is dependent on the type of data. Bertin ( 1977 ) suggests that data visualization primarily deals with two fundamental types of data, including data values and data structures. Ware ( 2012 ) proposes a similar suggestion that data can be split into entities and relations, in which entities refer to visualizing objects, and relations refer to structures and patterns relevant to entities. For example, people can be referred to as entities or a flock of birds can be referred to as a single entity. A house is built up by many different parts that have a structural relationship. Similarly, data usually has an attribute that is an emergent property of entities and may not exist independently. For example, the color of a car becomes an inseparable attribute of the car. Thus, traditional data visualization requires considering the type of data with regard to the entities, relation and its attributes.

Information visualization, which listed in Table  2.1 , has become an emerging area in the last decade. It is considered as information design, which is “about the selection, organization and presentation of information to a given audience” (Wildbur & Burke, 1998 , p. 6). Information design aims to design the efficient communication of information as its foremost task, which requests the content needs accurate and unbiased in the visual presentation. It attempts to present all the objective data required to enable the users to make some kind of decision (Wildbur & Burke, 1998 ). According to Wildbur and Burke ( 1998 ), information visualization can be categorized as three parts. The first refers to information as an organized arrangement of facts or data. For example, a timetable, a signing system and most maps fall into this first category. This organized information can let users freely to extract only that information which they need for a given purpose. The second includes “information presented as a means of understanding a situation or process, such as a guide-book, a bar-chart or a stage-by-stage description of how to get a machine to operate” (Wildbur & Burke, 1998 , p. 7). The third includes the design of control systems. For example, an input or feedback controls of a product. The common forms for information visualization involve Tables, Charts, Trees, Maps, Scatter plots, Diagrams, Graphs, as indicated in the table.

In scientific visualization, as indicated in the table, the forms of data visualization often refers to simulations, waveforms or volume, in which data is explored, transformed and represented as an image or simulation in order to obtain insight into the phenomena. It is quite common for scientific visualization to use 3D modelling to represent scientific data because data can possess various dimensions (Ware, 2012 ). In fact, there are many different types of forms for scientific visualization. There are more specific forms of data visualization, such as area chart, bar chart, bullet graph, Box-and-whisker Plots, Bubble Cloud, Cartogram, Circle View, Dot Distribution Map, Gantt Chart, Heat Map, Highlight Table, Histogram, Matrix, Network, Polar Area, Radial Tree, Streamgraph, Text Tables, Treemap, Wedge Stack Graph and Word Cloud (Data visualization beginner’s guide: a definition, examples, and learning resources, n.d.). These forms of data visualization are not common used in information visualization or scientific visualization. Table  2.2 presents the specific forms of data visualization along with the common forms, which have already listed in Table  2.1 .

Table 2.2

The forms of data visualization. It lists the common and specific forms of data visualization

By considering previous research, the common forms of scientific visualization will be identified and discussed in the following section (summarized in visualization section of Table  2.1 ).

Trees are considered one of the earliest visual illustrations of human thought systems (Lima, 2011 ). Its hierarchical structure has a great advantage in organizing, rationalizing, and illustrating information patterns and provides a key to interpret “evolving complexities of human understanding, from theological beliefs to the intersections of scientific subjects” (Lima, 2011 , p. 21). Humans often use trees as a symbol to represent classification of the natural world (Lima, 2011 ). In visualization, tree can also refer to “Tree-Map” visualization (Johnson & Shneiderman, 1999 ). The hierarchical structures involve two types of information, which include structural information correlated hierarchy and content information correlated each node. For example, the branched schema of trees has been used for organizing family ties, social structures, and species evolution. The Tree of the Two Advents by Joachim of Fiore in 1202 is an example of trees that illustrates key characters and institutions of the Christian salvation history. This tree displays key figures from top to bottom, repeating Jesus Christ, Ozias the Prophet, Jacob the Patriarch, and Adam. The image of Christ dominates the whole trees and the lower branches link the image of Jacob to the twelve tribes of Israel. The Christ in the middle links twelve branches that represent twelve churches (Lima, 2011 ).

Figure  2.2 presents a Tree-Map of architecture history in 1896. The Tree-Map was produced as a schematic diagram with what Banister Fletcher recognized as the tree branches of architectural styles. It was published in the first edition of Fletcher’s Architecture book. The author highlights a cross-cultural and historical evolution of architectural styles through a series of successive branches (Flood, 2012 ). The Tree-Map shows the evolution of the various architectural styles in five periods including Peruvian, Egyptian, Greek, Assyrian and Chinese and Japanese. At the base of the tree trunk is a number of individual architectural styles and the crown of the tree several styles are repeated. It presents an overview of the entire hierarchy and makes the navigation of each node much easier.

The Tree of Architecture (1896), from the book of A History of Architecture on the Comparative Method. London: Athlone Press, University of London. (Image courtesy Wikimedia Commons)

Gülsüm Baydar Nalbantoglu provided her comments on the diagram of The Tree of Architecture as the following:

The “Tree of Architecture” has a very solid upright trunk that is inscribed with the names of European styles and that branches out to hold various cultural/geographical locations. The nonhistorical styles, which unlike others remain undated, are supported by the “Western” trunk of the tree with no room to grow beyond the seventh-century mark. European architecture is the visible support for nonhistorical styles. Nonhistorical styles, grouped together, are decorative additions, they supplement the proper history of architecture that is based on the logic of construction. (Morrison, 2015 )

The visual metaphor of the tree is often used to represent a network of nodes that consists of root nodes and subordinate note within its branched schema in the field of data visualization. Trees are one of the most useful forms to depict data by starting from a root node and then tracing down to its subordinate node until all nodes have been explored (Spence, 2007 ). Trees can take various forms, such as TreeMap, SpaceTree, and StarTree, suitable for different data or information (Cawthon & Moere, 2007 ). The forms of trees have been used in many fields to represent data, including business and healthcare, amongst others. Spence ( 2007 ) presents a typical example of a tree map, which effectively displays a large amount of data for identifying patterns and tendencies (see the Status of Companies in Different Fields in the website of Smartmoney.com). Figure  2.3 shows a tree map that uses the hierarchical structure of the form of a tree—the roots, trunk, branches, but applies different visual forms. It presents clear information from financial markets that provide investors an instant overview of market activities.

The Smartmoney.com, (n.d.), The status of companies in different fields

A map is considered as a graphical depiction highlighting relationships between elements of space (objects or regions). The term of “map” comes from the Latin “ mappa mundi ”, in which “ mappa ” refers to cloth and “ mundi ” means the world. Therefore, a map can be defined as a 2D representation of the surface of the world. In fact, a map can present any space without consider context. For example, a DNA mapping or computer network topology mapping. However, a map often refers to geographic maps, which were used to help people define, explain and navigate through the world. As one of the most important inventions in human history, maps has been made and developed over centuries.

Cartography is a term that study crafting representations of the earth upon a flat surface. Cartography refers to the production and study of maps. The term comes from French cartographie , which was based on Latin carta . According to the International Cartographic Association (ICA), a map refers to “a symbolized image of geographical reality, representing selected features or characteristics” (Crampton, 2001 , p. 240). This definition has clearly presented that the symbols need to match to the referents. The visual variables, which “use discrete symbols (e.g., choropleth maps) to show discrete data such as sales tax rates, and continuous symbols (e.g., isarithmic maps) to show continuous data such as temperatures.”, have been used to “a set of map graphic building-blocks which match spatial phenomena” (Crampton, 2001 , p. 240).

Robinson ( 1952 ) argues that using more scientific approach to cartography, which focuses on “function provides the basis for the design” (p. 13). Maps are considered as map communication model (MCM) in the opinion of Robinson’s review of cartography. He argues that “a map maker makes the map created by a cartographer who is supposed to be sensitive to the capabilities of his envisaged map reader” (Robinson, Morrison, & Muehrcke, 1977 , p. 6). Robinson et al. ( 1977 ) further emphasizes that “corollaries of this view are a lessened concern for the map as a storage mechanism for spatial data and an increased concern for the map as a medium of communication …” (p. 6). This statement highlights a separation between the cartographer and the map users. The users can communicate information from the cartographer through maps. The MCM was considered to promote “the development of a philosophical and conceptual framework in cartography …” (Andrews, 1988 , p. 185).

The navigational maps are the most popular maps including road map, aeronautical and nautical charts railway or subway network maps, and even hiking or bicycling maps. For example, Google map is a web-based mapping service, which was developed by Google Company. It provides a range of services, including satellite imagery, aerial photography, street maps and so on. Most maps of the worlds are designed as four main maps, including political map that can show territorial borders of each country or city; physical map that presents features of geography, such as mountains, land, rivers, and also including roads, railways and buildings; topographic map that presents elevations and relief with contour lines; and geologic map that has a special purpose to display various geological features, which show rock units, bedding planes, structural features and plunge symbols. These different types of maps can service to different purposes. For example, a geological map can be used for geological exploration, while a political map may help people making plans to travel. Figure  2.4 shows a map of ancient Greece, which depicts the land of Greece surrounding by seas. It uses scale and coordinative techniques to present the geographical information in 1752.

Map of Ancient Greece, Graecia Vetus (Macedonia, Thessaly, Epirus, Achaia, Peloponnesus) in 1752

Maps were designed to static and fixed on paper or durable materials. The invention of computer technology facilitates the development of design of maps, such as dynamic and interactive maps. One of most important improvement use a variety of computer programs to generate a new form of maps. A basic function is interactive, which allows users to zoom in and zoom out the map. Users can easily search the name of locations they want to travel to in portable map device, which is known as a GPS (Global Positioning System).

Geographic Visualization (GVis) which uses the map’s power to explore, analyze and visualize spatial datasets to understand patterns. These developments are key components of a “maps as social constructions” approach, emphasizing the genealogy of power in mapping practices, and enabling multiple, contingent and exploratory perspectives of data. Figure  2.5 presents real time flight information in Australian airports during Covid-19 coronavirus outbreak in the early January 2020. It shows that much less flights over Australian sky than that before. It is because strict restrictions on travelling cause the large number of flights being cancelled.

Map of live flight tracking, a screenshot from flight aware website

Scatter Plots

A scatter plot is considered as one of the earliest and most widely used data visualizations, which is based on the Cartesian coordinate system (Ward, Grinstein, & Keim, 2010 ). It is a very useful form when multivariate data is presented with x or y coordinates (Cleveland, 1993 ). Each plot presents data in 2D or variables. A scatter plot is critically important in scientific research as it often presents data supporting important findings. Research from Weissgerber, Milic, Winham, and Garovic, ( 2015 ) emphasizes the importance of a scatter plot in scientific publication. They found that a scatter plot can enhance the understanding of published data for readers and “the increased flexibility of univariate scatterplots also allows authors to convey study design information” (Weissgerber et al., 2015 ). It is very flexible used in small sample size studies.

For example, Fig.  2.6 consists of a vertical scale and a horizontal scale that emphasizes that each of bivariate data (data for two variables) depends on the other, e.g. how Babinet points (the vertical axis) (the neutral points lie between 15° and 20° above the sun) depend on concentration (the horizontal axis) (Cleveland, 1993 ).

An exploratory scatter plot graphs the Babinet point against particulate concentration

Charts are the useful forms to represent data in line, bar, or slice forms. Traditionally, charts represent data in the form of a table, graph, or diagram. For example, Fig.  2.7 presents a chart, which was published in the New York Times in 1976, displaying information about the budget expenditures in localities in New York State, in which data is organized as bars that display information by their sizes. This form may be very useful in representing quantitative data that convey information about sizes or dimensions.

New York state total budget expenditures and aids to localities, New York Times, (1976)

As an example of visually representing information using different visual characteristics, Tufte ( 1997 ) presents a chart of History of O-Ring Damage in Field Joints, in which each bar of the graph in a chart as a small rocket that reflects a popular, recognized object rather than abstract forms (bars, lines, and triangles). This chart is effectively showing information about temperature and damage, which could help to facilitate possible insights into the data.

Another pioneer of exploring data visualization is Francis Galton, who made many contributions to data visualization and statistical graphics. It is well known for his role to develop the ideas of correlation and regression in graphic representation. He is also well known for his works on weather patterns began in 1861, which is shown in Fig.  2.8 . The chart presents barometric pressure, wind direction, rain and temperature on afternoon and evening on every day in December 1861. Each day in the chart, “the 3 × 3 grid shows schematic maps of Europe, mapping pressure (row 1), wind and rain (row 2) and temperature (row 3)” (Friendly, 2008 , p. 16). The chart presents weather information clearly, which people can easily understand “the series of black areas (low pressure) on the barometric charts for about the first half of the month, corresponding to the counter-clockwise arrows in the wind charts, followed by a shift to red areas (high pressure) and more clockwise arrows” (Friendly, 2008 , p. 16).

The multivariate weather chart of Europe, Galton ( 1863 ); it shows barometric pressure, wind direction, rain, and temperature for afternoon and evening on each day during the month of December, 1861

A table is one of the most common forms to display data within many fields. The data table can be an efficient form to comparative data on categorical objects. It usually uses rows and columns to represent data in two dimensions in the meaningful way (Harris, 1996 ). The quantitative data are placed in the square sited at the intersection of the row and column (named Table cells).

Traditionally in a table columns are used to represent dimensions or fields, and rows represent the actual data. It is an easier and simple visual form to represent data or dataset.

Compared to other visual forms, tables have a number of advantages including that they are “one of the best ways to convey exact numerical value” (Harris, 1996 , p. 387), and “assist the viewers in making comparisons, determining how things are organized, noting relationships between various sets of data” (Harris, 1996 , p. 387). Moreover, it can organize information where graphing might not be appropriate to the representation (Harris, 1996 ). Rows and columns of the table can use words, numbers or symbols.

Figure  2.9 is a typical table to present the data of the population, number of death and ratio to population in Ontario in 1886. On the left side of the table, there are lists of counties, which include all county in Ontario in Canada. The columns on the right of the table are information presented six categories, which are “population in 1886”, “number of deaths”, “ratio to population”, “phthisis”, and “anemia”. The data presented in the table is clear and accurate. People are able to easy learn the number of population and number of death from phthisis in each county in 1886.

Province of ONTARIO-vital statistics, Hardy ( 1887 )

A timetable is a type of table. It has been used to referring and managing tool for scheduled events, tasks and actions or appointment. The data is organizing within a table into chronological or alphabetical order helps users for quicker referencing. Timetables are basic way used to show the arrival and departure time, such as buses, trains and other forms of transportation. It is also an ideal tool for individual use for time management.

A diagram can be used in business-system analysis that consists of rectangles and lines illustrating information flow. For example, The Diagram Displays a Relationship and Information Flow for a Business System Analysis by Gerald Lohse emphasizes that the addition of background shading could enhance the understandability of an image by differentiating groups of information from one another (Keller and Keller, 1993 ). Figure  2.10 presents two statistical diagrams of the wholesale of poultry and game at Halles Centrales. Color coded reference and legend are included in the diagram. It presents the different poultry and the price in each month in 1888. The color of each column presents six poultry (chicken, pigeons, ducks, geese, turkeys and guinea fowl) and readers can understand the price of each animal during the months in 1888.

Atlas de statistique graphique de la ville de Paris. I. Annee (1888)

Before the computer technology is invented, most of graphs are competed by hand drawing, such as Fig.  2.11 . Although it can accurately present the data or information in form of graph, it can be very difficult to handle a large amount data, such as internet data flow. Figure  2.9 shows social network data visualization through computer software.

Social network visualization published in Grandjean ( 2015 )

A graph is similar to a diagram. In fact, the terms graph, diagram and chart are similar and can overlap. Graphics may have many purposes for presenting data. For example, a graph can help people to perceive and recognize the broad features of the data or information. It also can help people to look what are behind the data through the broad features (Anscombe, 1973 ). A graph may not present accuracy of data; however, it can provide the insight of data.

There is a classic example of graphs Interest of the National Debt from the Revolution by William Playfair in 1786 representing the British government’s debt interest, from 1688 to 1784 (Tufte, 2001 , p. 65). The use of the graphic clearly depicts the skyrocketing debt. Another example was the first case of cholera outbreak in Great Britain in 1831. The epidemic caused 52,000 people died within eighteen months. The water-born cause of the disease was not found until Dr. John Snow drew a famous map in 1855. It was considered as a landmark graphic discovery (Fig.  2.12 ).

The map of Leeds (1833), showing the districts affected by cholera

Waveform is a common form that is used in visual representation of the form of a signal, which refers to the shape of the time related to various quantities of data. It is often used in medicine, engineering, or earth science fields. For example, EEG data visualization is a typical waveform that is widely used in medical related research. Visualizing the stages of Non-Rapid eye movement sleep can be an example of waveform, which details the shape of a waveform with frequencies and bands showing electrical activity over time (Carskadon & Dement, 2011 ). There has an example of waveform, which has three waveform representing different studies. Figure  2.13 a presents a peripheral waveform (e.g., from the brachial artery); Fig.  2.14 b presents a waveform that is analyzed by a mathematical algorithm based on a generalized transfer function; a modulus amplification and phase lag characteristics are presented; Fig.  2.15 c presents a central waveform that is reconstructed. From this waveform, the information about the central systolic (SBP) and diastolic blood pressure (DBP), and pulse pressure (PP) are derived. The central arterial systolic pressure (CASP) is defined in the peak. The information has been effectively displayed in the waveforms.

An examples of peripheral waveform (a) (2016)

An examples of generalized transfer function (b) (2016)

An examples of central waveform (c) (2016)

A waveform is often used to visualize the data generated by earthquakes that are recorded and displayed as a seismic-wave form (Keller & Keller, 1993 , p. 72). The waveform is an effective way to represent the magnitude of an earthquake. In Fig.  2.16 , the horizontal earth movement is represented by dark color and the bigger waveforms present bigger magnitude of an earthquake. The left column presents the time in a day. This waveform is recorded in a station Jajag in Indonesia in 2018. It has effectively presented the data of earthquake magnitude and people can easy understand the size of magnitude.

The seismic waveform which recorded by GFZ German Research Center for Geosciences (2018)

Simulation is one of most useful forms of scientific visualization. One of the important advantages of simulation is that it enables viewers to understand and describe natural phenomena such as weather changes or the role of dark energy in the universe (Ahrens et al., 2010 ). Computer technology (CT) has been used to simulate natural phenomenon, such as a thunderstorm, global climate change (Thalmann, 1990 ; Rosenblum, 1995 ). Figure  2.17 is an example that a computer simulates climate, which depicts the carbon dioxide from various sources that are adverted individually as tracers in the atmosphere model. Carbon dioxide from the land is shown as plumes during 1900. This visualization adopts a three-dimensional model to simulate the climate, which provided a realistic environment for effectiveness.

Climate visualization of atmosphere (2007). (Image courtesy of Forrest Hoffman and Jamison Daniel of Oak Ridge National Laboratory)

Simulation is one of popular visualization of weather and climate related data. It creates the dynamic change of climate change. For example, NASA (The National Aeronautics and Space Administration) simulates a climate warming, which is shown in Figs.  2.18 and 2.19 . This data visualization shows how global temperatures have risen from 1950 to the end of 2013. Figure  2.18 shows that the color in ocean area is almost blue and color in land is yellow and blue in the year of 1950, but it has changed to yellow and red throughout continents and much of ocean in the year of 2013 (Fig.  2.19 ).

A screen shot of climate change in 1950, it was created by NASA

A screen shot of climate change in 2013, it was created by NASA

It is apparently that simulation can effectively present the nature phenomenon in a dynamic model, which has a significant different from other types of data visualization. In a simulation, people can see a process of the change throughout a certain period time. People may gain the insight or new finding from the simulation visualization.

Volume visualization is considered one of the most important forms of scientific visualization. It creates graphical representation of data set defined on 3D grids. Volume data set are multidimensional arrays of scalar data and vector data, which are typically defined on lattice structures representing values sampled in 3D environment (Knupp, 1999 ). Two basic types of volume data are scalar data and vector data (Bürger & Hauser, 2007 ). Scalar data contains single values for each point while vector data contains two or three values for each point, defining the components of a vector. As the development of 3D data acquisition field, the volume dataset grows fast. Volume data can be captured by many technologies, such as CT or MRI scan technology. Volume visualization has been explored in many domains and it is often used in representing medical related data, such as functional MRI data from scans, data from confocal microscopy (Kaufman, 1996 ).

Volume data visualization can be divided as five types, which are slice-based approach, emulation of other technology, volume rendering, indirect volume rendering and direct volume rendering (Tukalo, n.d.). Slice-based approach is a simple way to implement and has low computing complexity, however, the disadvantage of this visualization is that users need to restructure the whole object by the imagination as it only visualize a part of the object (Tukalo, n.d.). Most experts prefer to use emulation of other technology for visual analyses, as “the emulation allows experts to have a smooth transition to a modern technic from their old solutions” (Tukalo, n.d., p. 1). Volume rendering, the technique used in volume visualization, focuses on rendering a 3D data set into a 2D visualization. Indirect volume rendering has all typical features of 3D objects. Thus, this technology allows complex 3D analysis simpler. For direct volume rendering, it is considered as a powerful way to visualize volume data, as it has almost all the advantage of polygonal mesh models. It also can combine these models on the same scene (Tukalo, n.d.).

For example, Fuchs, Levoy, and Pizer ( 1990 ) create a 2D image of a human skull, which is rendering by a CT scanner. It demonstrates how volume rendering is used to present a tumor in purple in a human head and a radiation treatment beam in blue using ray tracer technique. There is another example of volume rendering in Fig.  2.20 , which is a visualization of inbound traffic measured in billions of bytes on the NSFNET T1 backbone for the month of September 1991. The volume range is displayed from purple (0 bytes) to white (100 billion bytes). It represents data collected by Merit Network Company.

Regional Networks Traffic in 1991. (Image courtesy of Merit Network, Inc., NCSA, and the National Science Foundation)

The forms in Table  2.1 could be used in both information visualization and scientific visualization. Data and dataset in both information visualization and scientific visualization can be different. That selecting which type of visualization tools depends on the purpose of visualization. The forms of tables and charts can be found in both visualization fields. However, some forms are only commonly used in one or the other. For example, simulation is one of the main forms used to represent phenomena in scientific visualization but it is seldom used in information visualization. Many data visualization techniques adopt hybrids of various forms, such as the combination of a chart and graph. The forms used in Table  2.1 are the common techniques for data visualization. Although the visual effects differ, all forms have similar characteristics, which emphasize the represented data having readability, recognizability and meaning (Kosara, Drury, Holmquist, & Laidlaw, 2008 ). In the next section, it will discuss the characteristics of traditional data visualization listed in the Table  2.1 .

The Characteristics of Traditional Data Visualization

There are various visualization techniques are available for design a good data visualization, which can help people produce something with important insights. To create good data visualization, it should firstly understand what the common characteristics of traditional data visualization are. As data visualization as a topic has received attention in many fields in the last decade, it requires high skills in storing, managing and analyzing huge data flow. However, it also needs the ability to visualize data effectively for communication (Yarmuluk, 2019 ). Table  2.3 outlines the key characteristics of traditional data visualization, which can be used in both information visualization and scientific visualization.

Table 2.3

The characteristics of traditional data visualization

The review of the literature suggests the common characteristics of traditional data visualization including readability, recognizability and meaning. These characteristics are based on data communication for comprehension and knowledge acquisition. Data visualization may has more characteristics, such as graphical design, color and interactivity, but the essential and common characteristics focuses on readability, recognizability and meaning. The selecting of visual forms in Table  2.1 for the transformation of data to information facilitates these characteristics.

Firstly, data visualization should be readable. Readability is one of the most important characteristics of data visualization, in which the visual representation is intelligible and can be understood easily. Secondly, users are able to recognize visualizing data. Recognizability is another important characteristic of data visualization that allows users to identify previous knowledge from the visual form. Lastly, data visualization should convey information people could understand. The visual representation of data assists users in understanding the meaning of data, which is an important characteristic for both information visualization and scientific visualization.

Data Visualization and Human Perception

Human perception plays an important role in data visualization. Ware ( 2012 ) suggests that perception can remarkably improve both the quality and the quantity of information being displayed. Perception refers to the way sensory data is organized, interpreted and consciously experienced. Also, perception can be defined as “the process of recognizing (being aware of), organizing (gathering and storing), and interpreting (binding to knowledge) sensory information” (Ward, Grinstein, & Keim, 2010 , p. 81). It is clearly that perception links to humane senses that produce signals from the environment through five senses including sight, hearing, touch, smell and taste. Ward et al. ( 2010 ) explain the notion of perception as the following:

Simply put, perception is the process by which we interpret the world around us, forming a mental representation of the environment. This representation is not isomorphic to the world, but it’s subject to many correspondence differences and errors. The brain makes assumptions about the world to overcome the inherent ambiguity in all sensory data, and in response to the task at hand. (p. 82)

This explanation emphasizes that human perception is a subject mental activities, which interpret data or information we received around the environment.

When quantitative data is presented in a graphical form, the users are asked to use their visual perception to make judgements about the visual form, to compare relative sizes, locations, orientations, colors, densities, textures of the elements of the visualization. In fact, human visual perception is a very complicated and subjective process, and that the effectiveness of the visualization for conveying objective understanding based on a wide range of subtle factors (Reuter, Tukey, Maloney, Pani, & Smith, 1990 ).

The Perceptual Process

In psychological study, researchers and scholars have been looked at how the human visual system perceives and analyses images for many years. One of key findings from the study is that “the discovery of a limited set of visual properties that are detected very rapidly and accurately by the low-level visual system”, which was named as “preattentive” (Healey, 2007 ). The term preattentive is related to attention. Healey ( 2007 ) investigates four theories of preattentive processing, which are feature integration theory, texton theory, similarity theory, and guided search theory.

Feature integration theory:

• It is a theory of attention developed by Anne Treisman and Garry Gelade. Treisman provided an important insight into the preattentive processing (Healey, 2007 ). Firstly, Treisman tried to determine which visual properties are detected preattentively, which were named as “preattentive features” (Trick & Pylyshyn, 1994 ). Secondly, Treisman formulated a hypothesis about how the human visual system performs this processing (Rheingans & Tebbs, 1990 ). Treisman suggests when human perceives a stimulus, features are “registered early, automatically, and in parallel, while objects are identified separately”. It has been regarded as one of the most influential psychological models of human visual attention.

Texton theory:

• Elongated blobs: specific properties such as hue, orientation, and width. For example, line segments, rectangles, ellipses;
• Terminators: ends of line segments;
• Crossings of line segments;

It is believed that only a difference in textons or in their density can be detected preattentively (Healey, 2007 ).

Similarity theory:

• This theory involves search time for perception. Some researchers investigated conjunction searches by focusing on two elements: (1) Search time may depend on the number of items of information required to identify the object. (2) It may depend on how easily an object can be distinguished from its distractors, regardless of the presence of unique preattentive features (Healey, 2007 ). Search time is based on two criteria: T-N similarity and N-N similarity.

Guided search theory:

• A new visual search theory was proposed by Jeremy Wolfe. He coined it as “guided search”. Guided Search Theory is a two-stage model of visual processing in which initial parallel search mechanisms direct subsequent serial search mechanisms (Wolfe, 2014 ). One example of the Guide Search Theory is the process of visually diagnosing for red circles within green circles as well as red shaped squares. The theory suggests that there are two sensory scanners, one which is sensitive to the color red, while the other only detects circular shapes. The information from the two detectors amalgamates the collected information and figures out the target through analyzing for the most fitted suspect. If the target is incorrectly identified, the next most suitable candidate will be chosen. (Wolfe, 2014 ).

Research on perception is also involves illusion, which is considered as a distortion of the senses. As the researchers attempt to find out how the human brain normally organizes and interprets sensory stimulation. This book does not attempt to address the theories of preattentive processing further, but it provides readers a sense of perception theories for understanding human visual system to data visualization.

The human perception involves signals that go through the nervous system, which in turn result from chemical stimulation of the sensory system. For example, vision involves light striking the retina of the eye; smell is mediated by odor molecules; and hearing involves pressure waves. Data visualization focuses on vision perception, which is the primary human sense. To understand how human to perceive the images can help people to understand the importance of data visualization in our life and society.

In summary, the forms and characteristics of traditional data visualization emphasizes comprehension of data by using related visual forms to represent information. Data visualization has many visual forms. The main visual forms include Trees, Scatter Plots, Charts, Tables, Maps, Diagram, Graphs, waveform, simulation and volume. Most of these visual forms can be used overlap. It emphasizes that good data visualization have the characteristics of readability, recognizability and meaning. This chapter also discusses how human visual system perceives data and information. It reviews that the definition of human perception and some important theories of preattentive processing, including feature integration theory, texton theory, similarity theory and guided search theory.

However, these visual forms and characteristics do not necessarily involve aesthetic considerations or approaches to design visualization. Data visualization has ignored the contribution of aesthetics to enhancing understanding or insight. Nevertheless, aesthetics has more recently been identified as having value to data visualization. This reflects two aspects that include the relationship between aesthetics and usability in the design of human-computer interaction (HCI) as well as visualization considered as artistic creation that emphasizes the value of visualization “in its own right and for its own purposes” (Van Wijk, 2006 , p. 428). The value of aesthetics within the HCI field was first introduced by Kurosu and Kashimura in 1995 (Tuch, Roth, Hornbaek, Opwis, & Bargas-Avila, 2012 ), demonstrating the importance of aesthetics in creating an interface that “significantly influences user’s perceived ease of use of the entire system” (Tuch et al., 2012 , Introduction section, para 2). Visualization as art, which gained attention during the early twenty-first century, positions the value of aesthetics as not only concerning images and visual representations, but also ideas, methods, and techniques. Van Wijk ( 2006 ) argues that in order for “aesthetic criteria for new methods to be effective guides…each link of the chain from idea, mathematical model, algorithm, implementation, to visual result is clean, simple, elegant, symmetric” (p. 428).

The next chapter will address how aesthetics underpins data visualization. It includes recent research on aesthetic approaches to data visualization and the contributions of aesthetics. Chinese aesthetic concepts and Kantian sublime will be explored in visualizing data.

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Graphical Representation: Advantages, Types & Examples

Graphical Representation: A graph is a categorised representation of data. It helps us understand the data easily. Data is a collection of numerical figures collected through surveying. The word data came from the Latin word ‘Datum’, which means ‘something given’. After developing a research question, data is being collected constantly through observation. Then the data collected is arranged, summarised, classified, and finally represented graphically. This is the concept of graphical representation of data.

Let’s study different kinds of graphical representations with examples, the types of graphical representation, and graphical representation of data in statistics, in this article.

What Are Graphical Representations?

Graphical representation refers to the use of intuitive charts to visualise clearly and simplify data sets. Data obtained from surveying is ingested into a graphical representation of data software. Then it is represented by some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. In this way, users can achieve much more clarity and understanding than by numerical study alone. 

Advantages of Graphical Representation

Some of the advantages of using graphs are listed below:

• The graph helps us understand the data or information even when we have no idea about it.
• It saves time.
• It makes it easier for us to compare the data for different time periods or different kinds.
• It is mainly used in statistics to determine the mean, median and mode for different data and interpolation and extrapolation of data.

Use of Graphical Representations

The main agenda of presenting scientific data into graphs is to provide information efficiently to utilise the power of visual display while avoiding confusion or deception. This is important in communicating our findings to others and our understanding and analysis of the data.

Graphical data representation is crucial in understanding and identifying trends and patterns in the ever-increasing data flow. Graphical representation helps in quick analysis of large quantities and can support making predictions and informed decisions.

General Rules for Graphical Representation of Data

The following are a few rules to present the information in the graphical representation:

• Suitable title:  The title of the graph should be appropriate that indicates the subject of the presentation.
• Measurement unit:  The measurement unit in the graph should be mentioned.
• Proper scale:   Choose a proper scale to represent the data accurately.
• Index:  For better understanding, index the appropriate colours, shades, lines, and designs in the graphs. 
• Data sources:  Data should be included wherever it is necessary at the bottom of the graph.
• Keep it simple:  The construction of a graph should be such a way that it is effortlessly understood.
• Neat:  The correct size, fonts, colours etc., should be chosen so that the graph should be a visual aid for presenting the information.

Types of Graphical Representation

1. Line graph 2. Histogram 3. Bar graph 4. Pie chart 5. Frequency polygon 6. Ogives or Cumulative frequency graphs

1. Line Graph

A line graph is a chart used to show information that changes over time. We plot line graphs by connecting several points with straight lines.  Another name is a line chart. The line graph contains two axes: \(x-\)axis and \(y-\)axis.

• The horizontal axis is the \(x-\)axis.
• The vertical axis is the \(y-\)axis.

Example: The following graph shows the number of motorbikes sold on different days of the week.

2. Histogram

Continuous data represented on the two-dimensional graph is called a histogram. In the histogram, the bars are placed continuously side by side without a gap between consecutive bars. In other words, rectangles are erected on the class intervals of the distribution. The areas of the rectangles formed by bars are proportional to the frequencies.

Example: Following is an example of a histogram showing the average pass percentage of students.

3. Bar Graph

Bar graphs can be of two types – horizontal bar graphs and vertical bar graphs. While a horizontal bar graph is applied for qualitative data or data varying over space, the vertical bar graph is associated with quantitative data or time-series data.

Bars are rectangles of varying lengths and of equal width usually are drawn either horizontally or vertically. We consider multiple or grouped bar graphs to compare related series. Component or sub-divided bar diagrams are applied for representing data divided into several components. 

Example:  The following graph is an example of a bar graph representing the money spent month-wise

4. Pie Chart

The sector of a circle represents various observations or components, and the whole circle represents the sum of the value of all the components. The total central angle of a circle is \({360^{\rm{o}}}\) and is divided according to the values of the components.

The central angle of a component\( = \frac{{{\rm{ value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{{\rm{total}}\,{\rm{value}}}} \times {360^{\rm{o}}}\)

Sometimes, the value of the components is expressed in percentages. In such cases, The central angle of a component\( = \frac{{{\rm{ percentage}}\,{\rm{value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{100}} \times {360^{\rm{o}}}\)

Example:  The following figure represents a pie-chart

5. Frequency Polygon

A frequency polygon is another way of representing frequency distribution graphically. Follow the steps below to make a frequency polygon:

(i) Calculate and obtain the frequency distribution and the mid-points of each class interval. (ii) Represent the mid-points along the \(x-\)axis and the frequencies along the \(y-\)axis. (iii) Mark the points corresponding to the frequency at each midpoint. (iv) Now join these points in straight lines. (v) To finish the frequency polygon, join the consecutive points at each end (as the case may be at zero frequency) on the \(x-\)axis.

Example: The following graph is the frequency polygon showing the road race results.

6. Ogives or Cumulative Frequency Graphs

By plotting cumulative frequency against the respective class intervals, we obtain ogives. There are two ogives – less than type ogives and more than type.

Less than type ogives is obtained by taking less than cumulative frequency on the vertical axis. We can obtain more than type ogives by plotting more than type cumulative frequency on the vertical axis and joining the plotted points successively by line segments.

Example: The below graph represents the less than and more than ogives for the entrance examination scores of \(60\) students.

Solved Examples – Basic Graphical Representation

Q.1. The wildlife population in the following years, \(2013, 2014, 2015, 2016, 2017, 2018,\) and \(2019\) were \(300, 200, 400, 600, 500, 400\) and \(500,\) respectively. Represent these data using a line graph. Ans: We can represent the population for seven consecutive years by drawing a line diagram as given below. Let us consider years on the horizontal axis and population on the vertical axis.

For the year \(2013,\) the population was \(300.\) It can be written as a point \((2013, 300)\) Similarly, we can write the points for the succeeding years as follows: \((2014, 200), (2015, 400), (2016, 600), (2017, 500), (2018, 400)\) and \((2019, 500)\)

We can obtain the line graph by plotting all these points and joining them using a ruler. The following line diagram shows the population of wildlife from \(2013\) to \(2019.\)

Q.2. Draw a histogram for the following data that represents the marks scored by \(120\) students in an examination:

Ans: The class intervals are of an equal length of \(20\) marks. Let us indicate the class intervals along the \(x-\)axis and the number of students along the \(y-\)axis, with the appropriate scale. The histogram is given below.

Q.3. The total number of scoops of vanilla ice cream in the different months of a year is given below:

For the above data, draw a bar graph. Ans: The following graph represents the number of vanilla ice cream scoops sold from March to July. The month is indicated along the \(x-\)axis, and the number of scoops sold is represented along the \(y-\)axis.

Q.4. The number of hours spent by a working woman on various activities on a working day is given below. Using the angle measurement, draw a pie chart.

Ans: The central angle of a component\( = \frac{{{\rm{ value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{{\rm{total}}\,{\rm{value}}}} \times {360^{\rm{o}}}\). We may calculate the central angles for various components as follow:

By knowing the central angle, a pie chart is drawn,

Q.5. Draw a frequency polygon for the following data using a histogram.

Ans: To draw a frequency polygon, we take the imagined classes \(135-140\) at the beginning and \(175-180\) at the end, each with frequency zero. The following is the frequency table tabulated for the given data

Let’s mark the class intervals along the \(x-\)axis and the frequency along the \(y-\)axis.

Using the above table, plot the points on the histogram: \((137.5, 0), (142.5, 35), (147.5, 40), (152.5, 55), (157.5, 50), (162.5, 40),\) \((167.5, 35), (172.5, 20)\) and \((177.5, 0).\)

We join these points one after the other to obtain the required frequency polygon.

In this article, we have studied the details of the graphical representation of data. We learnt the meaning, uses, and advantages of using graphs . Then we studied the different types of graphs with examples. Lastly, we solved examples to help students understand the concept in a better way.

Frequently Asked Questions (FAQs) on Basic Graphical Representation

Q.1: What are graphical representations? Ans: Graphical representations represent given data using charts or graphs numerically and then visually analyse and interpret the information.

Q.2: What are the 6 types of graphs used? Ans: The following are the types of graphs we use commonly: 1. Line graph 2. Histogram 3. Bar graph 4. Pie chart 5. Frequency polygon 6. Ogives or cumulative frequency graphs

Q.3: What are the advantages of the graphical method? Ans: The advantages of using a graphical method are: 1. Facilitates improved learning 2. Knowing the content 3. Usage of flexibility 4. Increases thinking 5. Supports creative, personalised reports for more engaging and stimulating visual presentations 6. Better communication 7. It shows the whole picture

Q.4: What is the graphical representation of an idea? Ans: The graphical representations exhibit relationships between ideas, data, information and concepts in a visual graph or map. Graphical representations are effortless to acknowledge.

Q.5: How do you do frequency polygon? Ans: Frequency distribution is first obtained, and the midpoints of each class interval are found. Mark the midpoints along the \(x-\)axis and frequencies along the \(y-\)axis. Plot the points corresponding to the frequency. Join the points, using line segments in order.

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