Research On Visual Design Of Big Data Visual Information Based On Semantic Mapping

With the rapid development of information and technology,research on big data information has gone deep into all walks of life.In order to discover the intrinsic value of these complex data quickly and accurately,the demand for visualization of big data information is increasing.As the data volume grows rapidly,it is a challenge for designers who design big data visualization information to display big data information scientifically and help user make effective decisions.Therefore,a proposal of design theory framework based on semantic mappings for big data visualization information design methods is proposed in this paper.Different degrees of research on big data visualization information in various fields are conducted at home and abroad,and numerous cases from visualization research labs and commercial field are selected for analysis in this paper.The theoretical research phase is divided into three parts.Firstly,semantic mapping methods and techniques are discussed carefully.Secondly,based on user questionnaire interviews and characteristics of big data information,the collected 34 groups of cases are semantically extracted and categorized,semantic spaces are established as well.Lastly,the information encoding features illustrated in various types of semantic information space are analyzed from seven attributes: location,shape,direction,texture,gray level,size and color,respectively.Therefore,the theoretical framework of semantic matching which is needed to design big data visualization information is summarized.In practical design stage,taking the analysis of the needs of peak-to-peak commuting users as an example,the summary framework recommendations are used to design the big data information.This sums up the need for big data visualization in the transportation sector and graphically summarizes case designs for all kinds of big data information involving traffic demand.