Infographic expression characteristics for effective visualization of big data

Abstract

Infographic, which expresses the meaning and the value of information easily and clearly, has become a very important part of life in the big data era. This paper focuses on finding the design directions of efficient big data infographic by analyzing the expression characteristics according to the type of visualized infographic Big Data. The types of Big data are classified into three types: map type, topic type, and flow type based on the concept of information message it conveys, and the proposed analysis is based on 12 factors in contraposition representing six different axes of the key elements that need to be balanced when designing infographic through the Visualization wheel Model. As the result of the analysis, it was confirmed that Map type should be centered on a ration information-oriented expression emphasizing functionality and figuration. The topic type should be designed to express originality and novelty. On the other hand, the flow type was confirmed that a natural expression that shows the variation of continuous data change to avoid redundant expression. Big data infographics should be represented according to the type of information by using expressions to inform functional and accurate information, and to express meaningful information with easy to understand and attractive information.