V4RIN: visual analysis of regional industry network with domain knowledge

• Published in: Visual computing for industry, biomedicine and art Vol. 7; no. 1; pp. 11 - 14
• Main Authors: Xiong, Wenli, Yu, Chenjie, Shi, Chen, Zheng, Yaxuan, Wang, Xiping, Hu, Yanpeng, Yin, Hong, Li, Chenhui, Wang, Changbo
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 15.05.2024; Springer Nature B.V; SpringerOpen
• Subjects: CAE) and Design; Computer Graphics; Computer Imaging; Computer Science; Computer-Aided Engineering (CAD; Data processing; Geographical distribution; Image Processing and Computer Vision; Interactive systems; Media Design; Network analysis; Original; Original Article; Pattern Recognition and Graphics; Regional industry analysis; Subject specialists; Vision; Visual analytics; Visualization; Visualization in finance; Visualization in finance; Visualization; Visual analytics; Regional industry analysis
• ISSN: 2524-4442; 2096-496X; 2524-4442
• DOI: 10.1186/s42492-024-00164-9

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.