How is data visualization created?

Data visualization Data visualization and information visualization infographic are two similar technical terms. In a narrow sense, digital visualization refers to presenting data in the form of statistical charts, while information visualization refers to visualizing non-digital information. The former is used to convey information, while the latter is used to express abstract or complex concepts, technologies and information.

Generalized data visualization refers to data visualization, information visualization and scientific visualization.

Data visualization originated from 1960s computer graphics. People use computers to create graphs and charts, visualize the extracted data, and present various attributes and variables of the data. With the development of computer hardware, people have created more complex and larger digital models, and developed data acquisition equipment and data storage equipment. Similarly, more advanced computer graphics technology and methods are needed to create these huge data sets. With the expansion of data visualization platform, the increase of application fields, the constant change of expression forms, and the increase of real-time dynamic effects and user interaction, the boundaries of data visualization are also expanding like all emerging concepts.

The familiar pie chart, histogram, scatter chart and histogram are the most primitive statistical charts and the most basic and common applications of data visualization. As a statistical tool, it is used to create a shortcut to quickly understand data sets and become a convincing means of communication. Communicate the basic information existing in the data. So we can see statistical charts in a lot of PPT, reports, plans and news.

But the most primitive statistical chart can only present basic information, discover the structure in the data and visualize the quantitative data results.

Facing complex or large-scale heterogeneous data sets, such as business analysis, financial statements, population distribution, media effect feedback, user behavior data, etc. Data visualization will face much more complicated processing conditions.

It may go through a series of complex data processing including data collection, data analysis, data governance, data management, data mining, etc., and then the designer designs a presentation form, which can be three-dimensional, two-dimensional, dynamic, real-time or interactive. Then the engineer creates the corresponding visualization algorithm and technical realization means. Including modeling methods, architecture for processing large-scale data, interactive technology, zoom-in and zoom-out methods, etc. Animation engineers consider surface materials, animation rendering methods, etc. And interaction designers will also participate in the design of user interaction behavior patterns.

Therefore, the creation of data visualization work or project needs the cooperation of professionals in many fields to be successful. Human beings can manipulate and interpret such diverse and complex interdisciplinary information, which is an art in itself.

In the development of data visualization, the application of science and engineering gave birth to a branch: scientific visualization-"using computer graphics to create visual images to help people understand the intricate and often large-scale digital expression of scientific and technological concepts or results".

Scientific visualization existed before the birth of computers. Such as contour map, magnetic field line map, sky map and so on. With the powerful computing power of computer, human beings can express complex liquid flow patterns and scientific models of molecular dynamics in three or four dimensions.

For example, using empirical data, scientific visualization can simulate natural phenomena in astrophysics that cannot be observed or recorded by human eyes (simulating cosmic explosion, etc.). ), geography (simulating greenhouse effect), meteorology (tornado or atmospheric advection); Using medical data (nuclear magnetic resonance or CT) to study and diagnose the human body; Or in the fields of architecture, urban planning or research and development of high-end industrial products. For example, in the process of automobile research and development, it is necessary to input a lot of structural and material data to simulate how the automobile deforms when it is hit. It is necessary to simulate the traffic flow in the design process of urban road planning.

Although the form of scientific visualization is unfamiliar to ordinary people, it is difficult to understand such charts as particle system, scatter plot and heat map without professional training. But in fact, the achievements of scientific visualization have penetrated into every corner of our lives.

In the early 1990s, the field of information visualization entered people's field of vision. Used to solve the analysis of "abstract" parts in heterogeneous data. It helps people to understand and observe abstract concepts and expand human cognitive ability.

The difference between scientific visualization and information visualization is subtle, because the processing objects of scientific visualization are mostly abstract concepts. There are also many similarities in means and technology. So the boundaries are blurred.

In foreign countries, many large enterprises and scientific research institutions will have relevant departments to conduct data visualization research, such as digital libraries. Media and government agencies will also visually analyze their own data, such as crime maps. On the Internet, websites that have mastered a lot of user activity information, user relationship networks or corpora, such as digg, friendfeed, flickr or large e-commerce websites, have experimental visualization projects. Unfortunately, domestic commercial or experimental projects in this field are still relatively blank.