多维数据可视化与同步,揭示隐藏的流行病信息

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Information Visualization Pub Date : 2024-09-18 DOI:10.1177/14738716241277559
Qi Zhang, Nikhil Maram
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引用次数: 0

摘要

要揭示数据中隐藏的信息,并为用户提供直观的决策反馈,可视化是不可或缺的。数据可视化对于将复杂的数据转化为各领域可操作的见解至关重要。近年来,冠状病毒疾病疫苗越来越多地为大多数人所使用。然而,美国疾病控制和预防中心(CDC)往往不能从侧面考虑冠状病毒大流行的多维数据,从而限制了医疗专业人员和个人与综合数据可视化进行比较和互动的能力。有效显示从多个来源收集的冠状病毒和疫苗接种数据对于解读流行病传播模式和疫苗接种效率至关重要。本文介绍了一个用于创新数据可视化的新平台,可为用户提供直观的反馈和完整的数据故事。我们设计的算法可在单个网页上无缝组合多个参数、同步属性并动态可视化随时间变化的数据。将所有属性整合到一张图上可能会因空间限制而令人难以承受,并且难以从过于拥挤的显示组件中提取关键信息,而我们开发的算法可以根据所有参数之间的关系和相似性对其进行分类、增强和分组。此外,我们还创建了一种并排可视化方法,可动态连接多个图像中的所有参数,以便进行数据探索、趋势比较、隐藏信息检测和对应分析。我们的平台可提供实时性能,使医疗保健专业人员能够做出明智的决策,有效地交流研究结果,并发现原始数据中可能不明显的模式。所提出的多维数据可视化算法在一般数据探索和揭示隐藏信息方面有着广泛的应用。
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Multidimensional data visualization and synchronization for revealing hidden pandemic information
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.
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来源期刊
Information Visualization
Information Visualization COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.40
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles on fundamental research and applications of information visualization. The journal acts as a dedicated forum for the theories, methodologies, techniques and evaluations of information visualization and its applications. The journal is a core vehicle for developing a generic research agenda for the field by identifying and developing the unique and significant aspects of information visualization. Emphasis is placed on interdisciplinary material and on the close connection between theory and practice. This journal is a member of the Committee on Publication Ethics (COPE).
期刊最新文献
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