Some people say that data visualization is just drawing pictures, so you can’t see the value of research. I was naive to think that data visualization is the transformation of data from cold numbers into graphics. At best, it is more colorful, more cool, and full of pressure.
In fact, a good visualization can bring people not only visual impact, but also reveal the law and truth contained in the data.
Meaning of Visualization
The ultimate goal of visualization is to gain insight into phenomena and patterns embedded in data, which has multiple meanings: discovery, decision making, interpretation, analysis, exploration and learning.
A concise definition is: enhancing the effectiveness of people performing certain tasks through visual expression. For example, several sets of data with the same statistical characteristics (variance, mean, etc.) can be visualized in completely different ways. As shown in Figure 1 below:
The significance of visualization lies in that, as an auxiliary tool of the human brain, it can keep part of the information for us. A good memory is better than a bad pen. Second, graphical symbols can direct the user’s attention to important goals.
Goals and effects of visualization
Traditional visualization can be roughly divided into exploratory visualization and explanatory visualization. According to the application, visualization has multiple goals:
- Effectively present important features
- Reveal objective laws
- Assist in understanding concepts and processes
- Quality control of simulation and measurement
- Improve the efficiency of scientific research and development
- Promoting communication, exchanges and cooperation
From a macro perspective, there are three functions of visualization:
- Information records
- Information reasoning and analysis
- Information dissemination and collaboration
Data visualization classification
Data Visualization consists of three branches, Scientific Visualization (Sci Vis, Scientific Visualization) and Information Visualization (Info Vis, Information Visualization), and later evolved into a third branch: This can be seen from the IEEE VIS conference classification.
Scientific visualization is oriented to scientific and engineering data, such as three-dimensional spatial measurement data of spatial coordinates and geometric information, computer simulation data, medical image data, and focuses on exploring how to present the laws contained in the data with geometric, topological and shape features.
Information visualization deals with non-structured, non-geometric abstract data, such as financial transactions, social networks and text data. Its core challenge is how to reduce the interference of visual confusion on large scale and high dimensional complex data.
In recent years, with the rise of artificial intelligence, people have gradually discovered that there are some things that machines can do better than humans, and also some things that need the help of 300 million years of human evolution. So the combination of visualization and analysis has given rise to a new discipline: visual analytics.
Visual analytics is defined as an analysis and reasoning science based on visual interactive interface, which integrates graphics, data mining, human-computer interaction and other technologies to form complementary advantages and mutual improvement of human brain intelligence and machine intelligence.
Similarities and differences between visual data analysis and data mining
Both visual data analysis and data mining aim at obtaining information and knowledge from data, but the means are different.
Visual analysis of data is to present data to users with easy-to-perceive graphic symbols so that users can interactively understand the data.
Data mining is to obtain data hidden knowledge automatically or semi-automatically by computer and give the acquired knowledge directly to users.
In other words, data visualization allows you to see the interface and is better suited for exploratory analysis of data. Data mining, on the other hand, is a pile of live but dark data that needs to be mined like coal to find gold.
References:
[1] Chen Wei, Zhang Song, Lu Aidong. Basic Principles and Methods of Data Visualization [M]. Science Press, 2013.
[2] http://echarts.baidu.com/examples/editor.html?c=scatter-anscombe-quartet
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