Information visualization is the graphical depiction of information using visual elements such as maps, diagrams, charts, and graphs. It is an interdisciplinary field with the goal of obtaining meaningful insights from data through visual representation. The techniques used are often related to areas such as information theory, cognitive psychology, perception and visualiztion, and computer science.
The term “information visualization” was first used by the computer scientist Ben Shneiderman in the 1990s. It is defined as “the use of computer-supported, interactive, visual representations of data to amplify cognition.” However, the visualization of data to uncover patterns and relationships in data has been around since the beginnings of written language. For example, early mathematical notation can be seen as an early form of graphical representation.
Information visualization has many applications, including data mining, knowledge discovery, and decision making. For example, data points can be visualized to identify major trends, outliers, and relationships between data points. Visualizations can also help to transform abstract concepts into more easily digestible representations.
In modern applications, various techniques are used to visualize data, including treemaps, scatter plots, and many more. Each type of chart is chosen to best illustrate the data at hand, allowing for more meaningful insights. Furthermore, the use of interactive tools, like zooming and panning, enhance usability.
Overall, information visualization is an important tool for the effective use of data. It allows for the fast identification of patterns, trends, and outliers, helping to make informed decisions. By using visual elements to represent complex data, information visualization provides a powerful tool for data exploration.
