Multidimensional data visualization and synchronization for revealing hidden pandemic information

被引:0
|
作者
Zhang, Qi [1 ]
Maram, Nikhil [1 ]
机构
[1] Illinois State Univ, Sch Informat Technol, 100 North Univ St, Normal, IL 61790 USA
关键词
Visualization; multidimensional data; synchronization; feature grouping; multi-parameter; dynamic linking; revealing hidden information;
D O I
10.1177/14738716241277559
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Visualization is integral to uncovering hidden information in data and providing users with intuitive feedback for decision-making. Data visualization is crucial for transforming complex data into actionable insights across various domains. In recent years, coronavirus disease vaccines have become increasingly available to much of the population. However, the CDC (Centers for Disease Control and Prevention) often fails to consider multidimensional coronavirus pandemic data from a side-by-side perspective, limiting the ability of medical professionals and individuals to compare and interact with comprehensive data visualizations. Effectively displaying coronavirus and vaccination data collected from multiple sources is essential for interpreting pandemic transmission patterns and vaccine efficiency. This paper presents a new platform for innovative data visualizations that offers users intuitive feedback and a complete data story. We designed algorithms to seamlessly combine multiple parameters, synchronize attributes, and dynamically visualize data over time on a single webpage. Instead of integrating all attributes into a single plot, which can be overwhelming due to space limitations and make it difficult to extract crucial information from overcrowded display components, we developed algorithms to classify, enhance, and group all parameters based on their relationships and similarities. Furthermore, a side-by-side visualization method was created to dynamically link all parameters in multiple images for data exploration, trend comparison, hidden information detection, and correspondence analysis. Our platform provides real-time performance, enabling healthcare professionals to make informed decisions, communicate findings effectively, and uncover patterns that might not be apparent in raw data. The proposed multidimensional data visualization algorithms have broad applications in general data exploration and revealing hidden information.
引用
收藏
页数:19
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