What Do You Mean by Information Visualization?

Information visualization is the study of visual or interactive demonstration of the abstract data to support human cognition. Both non-numerical and numerical data are included in the form of abstract data like geographic and textual information. Information visualization is entirely different from scientific visualization. Information visualization is applied when spatial representation is selected and Scientific Visualization is implemented when the spatial representation is already set.

Information visualization has risen from the research in computer science, interaction between humans and computers, psychology, business method, and visual design. It is applied as a critical element in digital libraries, data mining, scientific research, market studies, production control, and discovery of drugs. Information Visualization assumes that interaction techniques and visual representation take benefits of the broad bandwidth pathways of the human eyes into the brain, allowing the users to explore, see and understand the huge number of information all at once.

Related Article: Information Processing Cycle, Information Processing Theory

More info on Information visualization! 

Information visualization spotlight on the formation of approaches for assigning information that is abstract in instinctive ways. Visualization, statistics, machine learning, and data mining are the most basic approach for data analysis that is an integral part of problem-solving and applied research in the industry.  Among the above-mentioned approaches, visual data analysis or information visualization is most reliable on human cognitive skills. Visual data analysis also allows the invention of insights that are unstructured and actionable and are limited only to human creativity and imagination power. The analysts do not have to study any complicated process to interpret the visualization of the information.

Moreover, it is also implemented as a scheme for hypothesis generation that is followed by formal or logical analysis like testing for a statistical hypothesis. So we can say that information visualization is a unique way of representing data in a meaningful and visual way. Some common examples of visual information are scattered plots and dashboards. The method of information visualization begins with a proper understanding of the information of the target user groups. Qualitative research reveals when, where and how the visualization will be employed.

Information Visualization

The popularity of data visualization is increasing day by day in the creation of a website or an application. With the interactive implementation of information visualization, users can manipulate the information according to their requirements until the desired output is reached. The visualization of information is seen in the bar chart, choropleth of news, and continuous replay of any event. It helps the users to process information quickly, form a meaning shape and execute a decision in a comparatively shorter time.

Information visualization is a research field that perceptualizes the information and presents it to the common users. It requires representational metaphors, coherent models for interaction, and modes for comprehensible visual communication. Information visualization picks up large spaces of information that consists of more informational elements than the pixels on a single screen.

Criteria for Visualizing Information

Visualization must follow the below three criteria to be considered as a practical visualization. The criteria are:

  • Based on the non-visual data, the sole purpose of information visualization is data communication. This means that the source of data must be abstract or should not be instantly visible. Information visualization converts from invisible to visible sources, unlike image processing and photography.
  • Information visualization must create an image. The image may not be clear, but it must be a chief source of communication. If the image is very small and is not a part of the process, then it is not proper information visualization.
  • The most important part is that it must offer some way for the data to be recognizable and readable. Any non-trivial information of the data can be ruled out, but some relevant information of the data should be present that can be read.

Related Article: Data Visualization, Data Mapping

History of Information Visualization

In the 16th century, maps and geometric diagrams are used in the form of information visualization in exploration and navigation. From that time onwards, this process has evolved with time by including new methodologies and techniques into the equation. In the 17th century, information visualization discovered analytic geometry and formulas to calculate distance, space and time. It is also during this century, statistics were invented including probability, estimation, and demography. All these discoveries paved the way for information visualization and visual thinking. In 1786, the first graphics presentation was done by William Playfair.

In the 18th and 19th centuries, visual thinking was then implied in economic statistics and governmental responses like activity planning and policy-making methods. Nomograms that assist in complicated calculations and graphic forms like histograms scatter plots, and line graphs are included in information visualization.

In 1970, it was formally introduced for exploring the proper sense of data. In 1977, John Turkey discovered EDA or exploratory data analysis that is a predominant tool for analyzing and exploring the data. Right now, information visualization is included in everything starting from databases, text, networks, and organizations where large scales of data were processed every day.

The modern era began with computer graphics and scientific computing. ACM SIGGRAPH and IEEE computer society are dedicated to research on information, data, volume, and scientific visualization.

Goals of Information Visualization

Information visualization comes with many goals that make it important in daily life. Let us take a look at some of those goals.

  • It uses computer graphics to enhance human perception in understanding and organizing data regarding physical phenomena. It also performs similarly for all the elements of typically semantic domains.
  • Such visualization also helps those users who display behavior for information seeking. This has led to the goal of offering users with a communicative environment through information visualization.
  • Information visualization is implied in engineering and scientific disciplines to help the researchers understand the abstract information and the process they are studying.
  • The main goal of information visualization is to create the perfect metaphors for passing on the information accurately, implementing it in the system to properly extract the information and presenting the final result to the user.
  • Information visualization concentrates mainly on the use and design of the techniques for computer visualization like comprehension, analysis, text corpora, heterogeneous tabular data, and networked information systems.

Ways to Systematize Information Visually

The main step in the process of information visualization is information establishment for the targeted user group via qualitative research. Information visualization also determines the perfect approach for the information in an organization. The method of organizing and systemizing information visualization is steady and linear. Let us have a quick look at the ways as follows.

  • Defining the problem: First, the need of the user and how they want to want with the information visualization should be established. Complexity should be set according to the skill of the user.
  • Defining the data that is to be represented: The data needs to be categorized to determine the way of mapping. The different categories of data include original, quantitative or categorical data.
  • Defining the dimensions that are needed to signify a data: To decide the type of analysis like univariate, trivariate, bivariate, or multivariate that should be conducted, attributes and dimensions of the data need to be outlined first.
  • Defining the data structure: The data are commonly structured as temporal, hierarchical, linear, networked or spatial relationships.
  • Interaction from the specified visualization: Defining the amount of interaction that is required by the user from the information visualization is an important step to estimate which models among static, manipulable or transformable are the most effective ones.

Example in Everyday Life

Suppose we are searching for a place in Google maps. The map will offer two routes from the starting point to the destination. The first information with written instructions shows how to reach from point A to B directly without any diversions. This type of information is useful for those people who want to reach that particular place in minimum time, for example, a person going for an important business meeting.

The second image on the map shows a route with potential breakpoints in the journey. This type of visualization is beneficial for tourists who want to reach from point A to B while exploring some exciting places to see.

Both of these demonstrations are an example of everyday information visualization. The first one conveys a simple set of instructions with less graphical content. The second one conveys more information in visual image form to convey the quick processing of cognitive instruction.

Different Types of Information Visualization

There are different types of information visualization, each of them with exclusive features to understand and interpret the information. Below are some of the most famous methods and techniques for information visualization. Let us have a glance at them.

  • Cartograms: Distance or Area Cartograms are the copies of maps that represent parameters like population size, demography, traveling time, distance, and climate.
  • Choropleth: Choropleth is represented with dots of various colors to indicate examined variables like inventory stocks or sales level of a shoo-in each state of a country.
  • Pie Chart: The pie chart represents sectors demonstrating numerical values in which the angle and the length of the arc are proportional to the value mentioned.
  • Scatter Plot: Scatter Plots are a set of dots that are unconnected representing the values of the parameters.
  • Histogram: A series of the rectangle represents a histogram that includes both the periods as the width and the values of the parameters as the height.
  • Sunburst Chart: This chart represents a pie chart that describes the hierarchy data values with circles that are concentric in shape.
  • Dendrogram: The dendrogram represents a clustering of sets of data in a hierarchical form to determine the relationship between each other.
  • Tree diagram: This form of information visualization represents relations of the data structure in a tree-like shape usually from upward to downward or from left to right.
  • Node Link Diagram: It is a diagram in a circular shape that includes dots representing the nodes of the data and lines representing the node links.
  • Alluvial Diagram: This is a flow diagram representing the data structure changes under certain circumstances or over time.
  • Matrix: This type of diagram is used when multiple sets of data are connected through some common relations.
  • Time Series: This is the most common representation of information visualization for the continuous evaluation of time. This diagram can represent the number of visits, countries of visit and loading time of a website all in one place.
  • Connected Scatter Plot: This type of diagram is the visual representation of two variants taken from the same data set.
  • Polar Area Diagram: It is a standard pie chart diagram where the sector size is evaluated from the center along with the length of the arc and the angle.

Areas of Implementation

The different sectors where this technique is employed are:

  • Digital Libraries
  • Mining of data
  • Scientific research
  • Analysis of Financial data
  • Information graphics
  • Market studies
  • Health care
  • Mapping of crime
  • Manufacturing and Production control
  • Policy modeling and eGovernance

Why Information Visualization is Important?

It helps to make decisions that support reasoning and analysis. It also assists to discover, explore and look at things in a new way and always encourages creativity. Information visualization makes a point, tell a story and convey information from one person to another. It also helps to answer questions more conveniently and inspire people to think new thoughts.

This visualization of information expands the working memory and deduces the time for searching. It also includes the detection of pattern recognition and perceptual interference, monitoring and controlling the attention of the users.

Some Common Uses of Information Visualization

For Persuasion or Understanding through Presentation: We all know that sometimes a picture or a visual representation can help the users to understand a complicated subject that is difficult to explain verbally. One of the most famous representations of Information Visualization is the London Underground map. This map assists the tourists to move from one point to another using the underground system. In other words, this map breaks down complex information into simple visual data.

Explorative Analysis: Information visualization helps in explorative analysis and allows the users to discover the relationships in the data that may be present in them. For example, the map shows a geographic region of the USA with the latest frequencies of lung cancer. This type of explorative analysis of the information visualization helps users establish an accurate relationship between geography and the disease.

Confirmative Analysis: Information visualization can also be employed to confirm the analysis and understanding of the data by the user. For example, you can use confirmatory analysis to recognize the relationship of prices between the two stocks. You can plot the data on a diagram and check if the two stock prices are related or not.

Related: Online Information Visualisation Courses

Other uses

Apart from the above-mentioned benefits and uses, information visualization is also used for:

  • Recording important information of blueprints, tablets and satellite images
  • Processing the required information, returning feedback and interaction
  • Sharing, revising, collaborating the information
  • Seeing the invisible data

Information Visualization is designed to create a sensible data. It is used to establish a relationship between data, confirm various ideas about the data or easily explain the data. As the volume of information exponentially increases in every field, information visualization is increasingly becoming important in all sectors to persuade users with the required data. So we can conclude by saying that information visualization is a thoughtful process for proper presentation, understanding and analysis of the data and offering effective communication to gain insights.

Leave a Comment

Your email address will not be published. Required fields are marked *